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    "# Exploring PACE's Role in Cyanobacteria Detection\n",
    "\n",
    "**Author**: Bingqing Liu[![](orcid.png)](https://orcid.org/0000-0003-4651-6996)  (University of Louisiana Lafayette)"
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   "cell_type": "markdown",
   "metadata": {},
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    "This tutorial will show you how to use HypterCoast to explore the spectra of a cyanobacteria bloom. We will use blooms in the Gulf of Mexico as an example.\n",
    "\n",
    "![](https://i.imgur.com/uFDVyii.png)\n",
    "\n",
    "#### What is HyperCoast?\n",
    "HyperCoast streamlines the processing of hyperspectral data from existing spaceborne and airborne missions (e.g., PACE, EMIT, AVIRIS, NEON, and DESIS) and upcoming hyperspectral missions, such as, SBG and GLIMR.\n",
    "\n",
    "https://hypercoast.org\n",
    "\n",
    "![](https://i.imgur.com/HBMjW6o.gif)"
   ]
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    "Uncomment the following cell to install the HyperCoast package."
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   "source": [
    "# %pip install hypercoast\n",
    "# %pip install -U hypercoast"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import hypercoast\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read PACE level 2 data, which is non-grided nc file. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 2GB\n",
       "Dimensions:     (latitude: 1710, longitude: 1272, wavelength: 184)\n",
       "Coordinates:\n",
       "    longitude   (latitude, longitude) float32 9MB ...\n",
       "    latitude    (latitude, longitude) float32 9MB ...\n",
       "  * wavelength  (wavelength) float64 1kB 339.0 341.0 344.0 ... 714.0 717.0 719.0\n",
       "Data variables:\n",
       "    Rrs         (latitude, longitude, wavelength) float32 2GB ...\n",
       "Attributes:\n",
       "    long_name:      Remote sensing reflectance\n",
       "    units:          sr^-1\n",
       "    standard_name:  surface_ratio_of_upwelling_radiance_emerging_from_sea_wat...\n",
       "    valid_min:      -30000\n",
       "    valid_max:      25000</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-6cf7e735-7bcb-418d-9d8a-a944c7ef498c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6cf7e735-7bcb-418d-9d8a-a944c7ef498c' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>latitude</span>: 1710</li><li><span>longitude</span>: 1272</li><li><span class='xr-has-index'>wavelength</span>: 184</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-21c6fc0c-0840-4944-840a-ade718657468' class='xr-section-summary-in' type='checkbox'  checked><label for='section-21c6fc0c-0840-4944-840a-ade718657468' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f534a998-18e6-49b1-9b10-caec733b2511' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f534a998-18e6-49b1-9b10-caec733b2511' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb3891bc-7138-4775-9ce1-6cc7fccff978' class='xr-var-data-in' type='checkbox'><label for='data-bb3891bc-7138-4775-9ce1-6cc7fccff978' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd></dl></div><div class='xr-var-data'><pre>[2175120 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3584dee7-83e7-44fd-8cbe-95f0e787a6a5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3584dee7-83e7-44fd-8cbe-95f0e787a6a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-463d6b61-dd22-424a-9273-a930f18c0483' class='xr-var-data-in' type='checkbox'><label for='data-463d6b61-dd22-424a-9273-a930f18c0483' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd></dl></div><div class='xr-var-data'><pre>[2175120 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>wavelength</span></div><div class='xr-var-dims'>(wavelength)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>339.0 341.0 344.0 ... 717.0 719.0</div><input id='attrs-3c15da91-6e4b-4b06-9d8c-3435a5dfeb60' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3c15da91-6e4b-4b06-9d8c-3435a5dfeb60' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-54bb31c3-280b-4372-9dd8-58dd93d9bceb' class='xr-var-data-in' type='checkbox'><label for='data-54bb31c3-280b-4372-9dd8-58dd93d9bceb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>wavelengths</dd><dt><span>units :</span></dt><dd>nm</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>20000</dd></dl></div><div class='xr-var-data'><pre>array([339., 341., 344., 346., 348., 351., 353., 356., 358., 361., 363., 366.,\n",
       "       368., 371., 373., 375., 378., 380., 383., 385., 388., 390., 393., 395.,\n",
       "       398., 400., 403., 405., 408., 410., 413., 415., 418., 420., 422., 425.,\n",
       "       427., 430., 432., 435., 437., 440., 442., 445., 447., 450., 452., 455.,\n",
       "       457., 460., 462., 465., 467., 470., 472., 475., 477., 480., 482., 485.,\n",
       "       487., 490., 492., 495., 497., 500., 502., 505., 507., 510., 512., 515.,\n",
       "       517., 520., 522., 525., 527., 530., 532., 535., 537., 540., 542., 545.,\n",
       "       547., 550., 553., 555., 558., 560., 563., 565., 568., 570., 573., 575.,\n",
       "       578., 580., 583., 586., 588., 591., 593., 596., 598., 601., 603., 605.,\n",
       "       608., 610., 613., 615., 618., 620., 623., 625., 627., 630., 632., 635.,\n",
       "       637., 640., 641., 642., 643., 645., 646., 647., 648., 650., 651., 652.,\n",
       "       653., 655., 656., 657., 658., 660., 661., 662., 663., 665., 666., 667.,\n",
       "       668., 670., 671., 672., 673., 675., 676., 677., 678., 679., 681., 682.,\n",
       "       683., 684., 686., 687., 688., 689., 691., 692., 693., 694., 696., 697.,\n",
       "       698., 699., 701., 702., 703., 704., 706., 707., 708., 709., 711., 712.,\n",
       "       713., 714., 717., 719.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f5cceeac-dbe3-4cf1-bb5e-aa542bdff21c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f5cceeac-dbe3-4cf1-bb5e-aa542bdff21c' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>Rrs</span></div><div class='xr-var-dims'>(latitude, longitude, wavelength)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-74f45153-ffa6-45f2-b0b1-dad8fbd7d5f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-74f45153-ffa6-45f2-b0b1-dad8fbd7d5f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02a4a2c8-5d59-4606-a8cb-a4f54fb514f0' class='xr-var-data-in' type='checkbox'><label for='data-02a4a2c8-5d59-4606-a8cb-a4f54fb514f0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Remote sensing reflectance</dd><dt><span>units :</span></dt><dd>sr^-1</dd><dt><span>standard_name :</span></dt><dd>surface_ratio_of_upwelling_radiance_emerging_from_sea_water_to_downwelling_radiative_flux_in_air</dd><dt><span>valid_min :</span></dt><dd>-30000</dd><dt><span>valid_max :</span></dt><dd>25000</dd></dl></div><div class='xr-var-data'><pre>[400222080 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-053b38fa-90f3-4315-9d06-dd5571d8fe6c' class='xr-section-summary-in' type='checkbox'  ><label for='section-053b38fa-90f3-4315-9d06-dd5571d8fe6c' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>wavelength</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e6fe6c3e-6278-43e4-b1c3-578d794ed63a' class='xr-index-data-in' type='checkbox'/><label for='index-e6fe6c3e-6278-43e4-b1c3-578d794ed63a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([339.0, 341.0, 344.0, 346.0, 348.0, 351.0, 353.0, 356.0, 358.0, 361.0,\n",
       "       ...\n",
       "       706.0, 707.0, 708.0, 709.0, 711.0, 712.0, 713.0, 714.0, 717.0, 719.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;wavelength&#x27;, length=184))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ffc5e8a6-cd50-4f27-aba7-771fb9ad4a37' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ffc5e8a6-cd50-4f27-aba7-771fb9ad4a37' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Remote sensing reflectance</dd><dt><span>units :</span></dt><dd>sr^-1</dd><dt><span>standard_name :</span></dt><dd>surface_ratio_of_upwelling_radiance_emerging_from_sea_water_to_downwelling_radiative_flux_in_air</dd><dt><span>valid_min :</span></dt><dd>-30000</dd><dt><span>valid_max :</span></dt><dd>25000</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 2GB\n",
       "Dimensions:     (latitude: 1710, longitude: 1272, wavelength: 184)\n",
       "Coordinates:\n",
       "    longitude   (latitude, longitude) float32 9MB ...\n",
       "    latitude    (latitude, longitude) float32 9MB ...\n",
       "  * wavelength  (wavelength) float64 1kB 339.0 341.0 344.0 ... 714.0 717.0 719.0\n",
       "Data variables:\n",
       "    Rrs         (latitude, longitude, wavelength) float32 2GB ...\n",
       "Attributes:\n",
       "    long_name:      Remote sensing reflectance\n",
       "    units:          sr^-1\n",
       "    standard_name:  surface_ratio_of_upwelling_radiance_emerging_from_sea_wat...\n",
       "    valid_min:      -30000\n",
       "    valid_max:      25000"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RGbBx40Z07ty53p+xHrxeeumlGDlyJE488cQ6g9dFixahffv2aN26NQYPHozbb78d7du3z/p65eXlKC8vT//dP4Nh48aNaNWqlfr/ACIiIiKKpKysDF26dEFBQUGDP2s1eF2wYAHeffddrFixos7vjxgxAmeeeSa6du2K9evX4+abb8bQoUOxcuXKrGeQT5s2DVOmTKn19VatWjF4JSIiIhIslxJPa8fDbty4EUceeSReffVVHHbYYQCAIUOGoF+/fpg5c2adv1NaWoquXbtiwYIFGDNmTJ0/UzPz6kfyO3bsYPBKREREJFBZWRkKCwtzitesZV5XrlyJrVu34ogjjkh/raKiAkuWLMEDDzyA8vJyNG7cOON3iouL0bVrV3z88cdZXzcvLy9rVpaIiIiI3GYteD3hhBOwZs2ajK+dd955OPjggzFx4sRagSsAbNu2DRs3bkRxcbGpyyQiIiIiQawFrwUFBejbt2/G11q0aIG2bduib9++2LVrFyZPnowzzjgDxcXF2LBhAyZNmoR27drh9NNPt3TVRERERGST9W4D2TRu3Bhr1qzBvHnzsH37dhQXF+P444/HU089ldNONCIiIiKKH2sbtkwJUgBMREREROYFidfEHA9LRERERNQQBq9ERERE5AwGr0RERETkDAavREREROQMBq9ERERE5AwGr0RERETkDAavREREROQMsYcUEBEREbmsotLDP9d/ha07v0f7gnwc1b0NGjdK2b4s5zF4JSIiIlKs5INSTPnbhyjd8X36a8WF+bj11N4Y3rfY4pW5j2UDRERERAqVfFCKi//4bkbgCgBbdnyPi//4Lko+KLV0ZfHA4JWIiIhIkYpKD1P+9iG8Or7nf23K3z5ERWVdP0G5YPBKREREpMg/139VK+NanQegdMf3+Of6r8xdVMwweCUiIiJSZOvO7IFrmJ+j2rhhi4iIiLRI4m779gX5Sn+OamPwSkRERMoldbf9Ud3boLgwH1t2fF9n3WsKQFFhVSBP4bBsgIiIiJRK8m77xo1SuPXU3gCqAtXq/L/femrv2GegdWLwSkRERMpwtz0wvG8xZv30RygqzCwNKCrMx6yf/ijWmWcTWDZAREREygTZbT+wR1tzF2bY8L7FGNa7KHE1vyYweCUiInKM5I1Q3G2/V+NGqVgH6LYweCUiInKI9I1Q3G1PurHmlYiIyBEubITyd9tnywOnUBVsJ2m3fUWlh2XrtuG51ZuwbN22WNf7msDMKxERkQMa2giVQtVGqGG9i6yWEPi77S/+47tIARnXm8Td9tIz5S5i5pWIiCgE09k0l44d5W77Ki5kyl3EzCsREVFANrJprm2ESvpue1cy5S5i5pWIiCgAW9k0FzdC+bvtR/frhIE92iYqSHMpU+4aBq9ERBQ7upb0bTbg50Yot7iWKXcJywaIiChWdC7p22zAz41QbnExU+4KZl6JiCg2dC/p286muboRKomtopgp14eZVyIiskrVaVEmNshIyKa5thEqqa2imCnXh8ErERFZozKwMbGk72fTtuz4vs4gOYWqLKjubJorx476mfCaY+VnwiVni1XwM+U17/GiBATvOjF4JSIiK1QHNiaW9JlNyx1bRVVxLVPuAta8EhFRKFHqGHXs2je1pO9q3alpbBW1V5JbhunAzCsREQUWdblfxxK/ySV9Sdk0VTXDqtne3EbxxeCViCiBogQ8Kpb7dQQ2ppf0JdSdSt4MJWFzmyRSJxkuYvBKRJQwUQIeVXWMugKbJG2Qkb4ZSsrmNgkkTzJcxJpXIqIEidoHVVUdo84emMP7FmPpxKGYf+EA3De2H+ZfOABLJw6NVZBg86SvXPmZcAC1/n9O0uY2W8cJxxmDVyIiRwXdMKUi4FG13K87sDG9QcZ0E35XNkMlfXObC5MMF7FsgIjIQWGWIVVsklK53B+XJX4bS8IubYaStLnNNJvHCccZg1ciIovCbOIIW+uoIuBRXcfoemBjq+7Utc1QEja32eDSJMMlDF6JiCwJk7GLsmFKRcCjY0e/7sBG1y5vm034uRnKDa5NMlzBmlciIgWC1jyG3cQRpdZR1SYpl+oYSz4oxaDpCzHukeW4csFqjHtkOQZNX6hkk4zNulNXN0OZrg22TefGxCRj5pWIKKKgGdQoGbsoy5Aqs6YuLPfrXtK3vSTsWs1wEttF8ThhPRi8EhFFECZAirKJI+oypMqAR9dyv4plfhNL+hKWhF2YRADye9Lq5NokwwUMXomIqgkSOIUNkKJk7FTUOkoOeFRl50zs8pZSdyp9M5TN2mApJL/nXMTglYjofwUNnMIGSFEydqqWIXUEPFEzpiqzcyaW9LkknBu2i6oifZLhEm7YIqJYMrGBKmyAFHUTh8QNU1E3Rqlu5m5qSV/S/xdSN0PZrg2m+GHmlYhix9QGqrABkoqMnaRlSBUZU9XZOZNL+hL+v5C8GUpCbTDFCzOvRBQrYTKoYVseRcmgqsjYqT4CNUzmTlXGVHV2znQrKdPH0VYXtu2aKWwXtZfU7LhrmHklIpHC1E+a3kAVNYMqIWPnC5u5U5Ux1ZGdS8Iubxc2Q7E2uIrk7LhrGLwSkTimA6kogVPUAEn1Jg6Tx80C6jKmupb5bUwQdJ3oVRdXNkMlYSJRnyS3CtOBwSsRiWIjkIoaOEnJoJo+bhZQlzHVmZ0zucvbdHbNpc1QUt4nprmQHXcNa16JSAsb9ZNRN1AB4esjVdY8hhk7G8fNAmrrGSXt3A/DRu2pa5uhbNYG22LzGOG4YuaViJSzVT8ZJYMqZVnTdPY0auZOdcbURHZOx7K+reyalIMSKDuXsuOuYPBKRErZrJ90fQNV2LGzedwsoD7w17nMr2tZ31btKTdDyedadtwFDF6JKEOUrJSE+klJG6hMHDUL2D9uFrAf+OdC56YZm9k1KasGQZjc1GYbs+PqMXglorSoWSmby/7VSQikTB01C8g4btZ/LR0ZUxWBju5lfdvZNQn3fK6S1jKK2XH1uGGLiACo2WyiatkfiN5Y3uYGKpNHzQLxPG7WF/XYWZ/uTTMSGvG7sBlK+oEKukh+j7mImVciUpaVkrDsr5r0o2aB+B0361O5zK97WZ/ZtYYlvWWUxPeYqxi8EjlOxZKqqs0mcVr2B8IFT2HHUkWv2ahBv4QDE6r/rspAx8SyvrSJlzSuHKigk8mew3HG4JXIYapqx1RlpaTXT5rYQGXrqFlATtAP2K+frsnUphkp/x9I3BDFllGkCoNXIkepXFJVmZWSmn0ytYHK5lGzgL1uCdWpuDdVBzoml/VtZ9ekboiyvalNCokTC9cweCVykOolVdVZKZ3ZpzAP/jDBFI+aDRcASaqfrknqxEolne3AomLLKLkTC9cweCVykOolVR1ZKR3ZJ5OnT0U9ajbKWErI3IUNgKTVT9ckZXKgg/QNUUnf1CZ5YuEatsoi0izMOfUN0VE7Jr2VS9gWO2FbJEVpfSRpLIPefw0FQEBVAJTtdVTXTwPR26bV9dqmWkrpeP9no7sdmAqS3hsmRX1fUSYxmddp06Zh0qRJuPLKKzFz5kwAgOd5mDJlCh5++GF8/fXXOProo/G73/0Offr0sXuxRDnStUSkq3ZM6rnyNk6fcv2oWSDc/Rc1c5qE+ulcmV4idmVDlIT3hmnstKCWiOB1xYoVePjhh3HooYdmfP2uu+7CjBkz8Pjjj6NXr16YOnUqhg0bhrVr16KgoMDS1RLlRucSkc7aMYnnyts6fcrVo2aB8Pdf1ADIpfppnWwsEbu0Icp2aYxprkwsXGE9eN21axfOPvtsPPLII5g6dWr6657nYebMmbjxxhsxZswYAMDcuXPRoUMHPPnkk/j5z39u65KJGqS79szF2rEoH+YqTp9yeQOVqYMSgOgBkCv10z4dO79t1Z5yQ5RcLk0sXGC95vXSSy/FyJEjceKJJ2Z8ff369diyZQtOOumk9Nfy8vIwePBgvPXWW1lfr7y8HGVlZRl/iEwzUXtmunYsSu1e1HovFadPAeFrJ107ajbK/afimFNX6hpVHT1bk63aU511wjqZrAu2RcLxwXFiNfO6YMECvPvuu1ixYkWt723ZsgUA0KFDh4yvd+jQAZ9++mnW15w2bRqmTJmi9kKJAjK1RGQqK2i74byE06dUMJVBjXL/qcqcSshY10fnsr7NJWIp93quktI6ysXVMsmsBa8bN27ElVdeiVdffRX5+dmzKqlU5v+RnufV+lp1N9xwA66++ur038vKytClS5foF0xOM90U2uQSke7aMQkN5+Nw+pTJo2aj3n+qAiDbp6TV9xo6l/VtLxHbvtdzlbTWUa5NLCSzFryuXLkSW7duxRFHHJH+WkVFBZYsWYIHHngAa9euBVCVgS0u3vt/6NatW2tlY6vLy8tDXl6evgsn59iY2cel9kxSw3mXT58ynUFVcf9JDIBUvZd17/yW8P6XviFKek9aXSS+r1xkLXg94YQTsGbNmoyvnXfeeTj44IMxceJEHHDAASgqKsJrr72Gww8/HACwe/duLF68GNOnT7dxyeQgWzN7CUtEKjJU0hrOS3nwSz9qVtX9pzoAinJPqnwv617Wl/D+ly7JraOkTyxcYC14LSgoQN++fTO+1qJFC7Rt2zb99auuugp33HEHevbsiZ49e+KOO+5A8+bNcdZZZ9m4ZHKM7Zm9zSUiVRkq1Q3nVXyY2w6oXDlqVtoSZZR7UvV72cSyvqTxN102lQu2jqIorLfKqs91112H7777Dpdcckn6kIJXX32VPV4pJxJm9jYyhSozVHFvOG9qA5WtDKqkTHWUe1L1e9nUsr6E8Ze6Icp2XbAEEicVrhAVvC5atCjj76lUCpMnT8bkyZOtXA+5TcrM3uQSkeoMVZwbzpvcQGUzg2rzoAT/d6Lek6rfyyaX9W0uEUveECWhLtgmqZMKV4gKXolUSuLMXnWGypWG80GDKtMbqOKQQbVxOppPx3tZ4kqASrbLphqS5LpgyZMKVzB4pdiSPrPXsWSkI9ss/UM+TFBlowWVlAxqmPvO1uloPl3vZdOTApPLxBLKphoi/dmig/RJhSsYvJISEmt3JM/sdS0Z6co26/6QD3v/hA2qbLWgsp1BDXPfRf2wVXFP6nwvm1rWN71MLKVsqiG23xOmuTCpcAGDV4pMcu2OxJm9ziUjndlmXR/yYe+fKEGVzRZUtjKoYe8726ej+SS+l3NlY5nYpbKpJLWOcmVSIR2DV4rEhdodSTN73UtGkrPNdYly/0QJqlxvQWWqSwIg43Q0n4n3supVJFvLxNLLppLKpUmFZAxeKTSXanekzOxNLBlJCK5yEfX+iRJUubyBymSXBEDO6Wg+ne9lHatItpaJXZvIAjLLz1TjpEINBq8UGmt3gjO1ZGQyuAr7gRP1/okaVEnZQAXkPoamuyQA8TsdLRtdq0g2l4ldmcgCssvPVHJxUiERg1cKLS61OyZn+yaXjExkm6N84ES9f1QEVRICqiBjaKNLguTT0QA171+dq0i2l4kl3OMNcaH8TCWXJhVSMXil0Gw/lFUwPduP05JR1A+cqPePqqDKZhP/oGNos0uCxA9bVe9fnatIEt7zUsqm6uJS+ZlKLkwqJGPwSqFJeChHYWO2L2HJSEqmSlXmVEpQZWITlc0uCdI+bFW+f3WuIkl4z0uW5PIzyZMK6Ri8UmguP5RtzvZtBlySMlWq7h8dQZWJNlRhxtB2lwTVH7ZhJ1Kq37+6V5EkTbKkbYqKS/kZmcXglSKR9FAOwvZs30YWS2KmStX9ozKoMtWGKswYutwloaYoEynV718Tq0gSxl7ipqg4lJ+ReQxeKTIJD+WgJMz2TS4ZSc5USbp/TLahCjuGLnZJqCnqREr1+9fUKpLNZWKpm6JcLz+LSlom3BUMXkkJ12p3kjbbl56pkrAL3XQbqqglALYDfhsno/l0vH9dXUXKheRNUS6Xn0UlMRPuCgavlEiSZ/s6ZuKuZqrCCvOhYDqDGnUMXc3iqZhI6Xr/SpgU6GC7TKohcZ44ZCM1E+4KBq+kncRlEanBl66ZeJIyVWE/FGxlUCWMYZD3qM2T0Xw637+mJgUmn4sSyqQaEteJQ10kZ8JdweCVtJK8LCIlcPDpnIm7mqkytfQP2Mug2v7QDvoetX0ymk/a+zcI089FV8qkXCs/C0t6JtwFDF4VkphhtMmFZRHbgYNP90zcxUyVyaV/wG4GVdUYmmjxJeFkNJ+U928QNp6LksukksiFTLh0DF4VkZxhtMGlZREJs30TM3GXMlWml/6B5GVQw75HpZyMVv31dL1/VSckbD0XpZZJJZUrmXDJGLwq4EKG0TQuiwRjaiZuMsCy0YA+6odCkjKoYd+jcTsZLRsdCQmbz0UXxtwX91VMZsKjY/AakUsZRpPisixi6iFqciZuItNsqwG9qsAqCRnUsO9RySejqaIrIWH7uSh5zH1JWMVkJjy6RrYvwHVBPmiTJA7LIiUflGLQ9IUY98hyXLlgNcY9shyDpi9EyQelyv8tP+jK9qhKoeoB7sJM3P/gr/m+8D/4Gxo/FUv/AGqNZZAPBT/AH92vEwb2aBt5qXjZum14bvUmLFu3DRWVdYWZVcKMXdhnUJT3qJ/FKyrM/F5RYX6gwE7lOPuCjHe2369vMgBUTQaCvi4g47moY8xVifrscImq91BSMfMake2ZtFSuL4uYLgWJy0xcQgN6ScujQbJILh2SAMjM4qnI2ulc2nf9uahTElcxJb6HXMHMa0QSZtISqcqA2aAz81If2zPxqBkrQM1KhIos9PC+xVg6cSjmXzgA943th/kXDsDSiUONB65BskimM6gq3qOqs3hR7kFVWTudCQmXn4u6JXUVU3ImXDJmXiPiTDo7SRmwIGxvqrAxE1dVZyapAb3q2l7djfyTfEgCEO0eVJm1052QkDLm0jZFcRWTgmDwGlFclnt1cXFZxPZD1HTrLpUlEnFtQG+ikX9SD0kAot+DKiecJhIStsdc4qaopK9iSptMSMfgVQFpH7TSSOijGkSSHqKq68xcaEAvtZF/Eg9J8H8n6j2ocsJpKiFh67kotbVjklcxJU4mpGPwqojtmTSpI/UhqmNmrrpEQnoDesmN/F3PoIb9AFZxD6qecMY1ISF5U1RSVzGlTiakY/CqkGsZRgkkLpVIfIjqmpnrKJGQ+sHvQiN/VzOoUT6AVdyDOiacpicDJp6F0g+Pkfrs0EXyZEI6Bq8GSQzUbJK8VCLpIapzZq6rRELnB7/JpWkbjfxdy6BG/QBWcQ/qmnCaSkiYehbarufPhe373yTpkwnJGLwaIjlQs8GFpRIJD1HdM3OdJRI6PvhNL02raOQfZgLkUgY16gewqntQ0oQzCJPPQlfq+ZOyiunCZEIqBq8GuBComeTSUonth6jumbnEEolsbCxNu9zIX/oxsz6V96Du8Va9emb6WSi1nj+pXJlMSMRDCjSz1fBesqQ2ow7DxMzc5OEIYZvQR30fxamRfy5cOWbWp/Ie1DXeOo6LNv0sdO2QBBUHp0gWp2PBTWPmVTPWtNQWh6USU/XLpmbmJjKEUUpnbC5NS1mOzvWec+2YWZ+EMp1sdK2e2XgWSrmfG5KEUjuXVr6kYfCqWRwCNdVcXyox+VA1ucyns0Qi6oe/7aVp24FVkHvOdH2vyg9gm6ei1fcaupb2bT0Lbd/PDUlSqZ0rkwlpGLxq5nqgpoPLdVemH6pxmJmr+PBXuTRtuw0VECyoCnrPJf2YWZ+qSabO1TObz0Lb9fzZuLQnQhXpkwmJGLxq5nKgpourAZmth6rtwCBq9krFh3+clqaDBFVh7rkkHzPrUznJ1Ll65uqzUKekltpJnUxIxeBVMz6c6mY7IAvD5kPVVmCgInul4sNf6tK07lZUYe65OBwzC4SfNKmeZOpePZPwLJTUg5yldpQLBq8GSHg4SSQpU5ML2w9V0zNzVdkrVR/+0t5HJlpRhbnn4pBBtbm5ryYTq2cutVTTLcmldpImEdIxeDVEwgeCRC4tlSTpoaoye6Xyw1/K+8hUM/+w95yUDGqYD2Pbm/tqMrV6ZuNZKHFjVFJL7aRNIqRj8GqQS4Ea1Sb1oapjtq4ye6X6w1/H+yjIGJpsRRW1BMCVDgk+KZv7apKW9VdB6saoJJbaSZxESMfglcSStoQi8aGqa7auOnsl+cM/6BiabEUV9Z6zNWEO+2EsaXNfTbYnA6pJ3hgl+XmhmtRJhHQMXgWRFqzZJHUJRdJDVedsXVf2StqHf5gxNN2KSsI9ZyIzDcjb3FfXa5sI5Ex8Ftiu4W+IxOeFDpInEZIxeBVCarBmg/QlFAkPVd2zdV3ZK10f/mE+7MOOoY1WVC5t6InyYRzXzX1BmPoscKGGPwmldtInEVIxeBVAerBmkitLKLYfqrpn6xJLJLIJ+2EfdgxttaKysYnKZGYaiOfmviBMfhZIreFPGhcmERIxeLXMlWDNFC6h5MbEbN2F7FWUD/uwY+hyKyrdByQA0T6MXdjc51O9tG/6s8ClCWqccRIRDoNXyxisZXJ9CcVU3bKp2bqJQMtWM/ooYyihFZXEAxKA6B/GrkyaVC/t2/gscGGsgXjvB+EkIhwGr5a5Hqyp5vISism6ZZOzdZ3ZK5vN6FUEWS5kUAFzByQAaj6MJS/561rat/VZIHmsgWTsB3FlEiEJg1fLXA7WdHB1CcV03XIcZuu2m9GrGENX6lBNHpAAqPkwtt3PN9vv61rat/lZYLuGP5sk7QeRPomQhsGrZa4Ga7q4GJTZqlu2OVuXEASo+LCXkPEwUYdq+oAEQN6HsYoMns6lfX4WZErifhCpkwiJGLxa5mKwppuEgCIIm3XLNgIEKUGAqg9728v/JupQbRyQ4L+Gqns+yoRJVQZP59I+PwsycT8I1YfBqwCuBWsmSMva1Md23bLJ2bqkIEDlh72NIMtkHarLByQA0SZMKjN4upf2JYy3lM1Rtp+rJBuDVyFcCtZMcWUJJSl1yxKDAAkf9tUFCbJM1qG6ekACEH3CpDKDZ2Jp3/ZKgJTNUUl5rtZFygRCMgavgrgSrFGmpNSqSQ0CdH3Y625FZboO1bUDEvyfjzphUpnBM7W0b+OzQNrmqKQ8V2uSNIGQjMErOUfarFRqrZrqcZIcBKj+sDfRispGHaprWT0VEybVGTxp2X4VJG6Okvpc1UnaBEIyBq+OkBaw2SJ1VirtA03HOCUlCDDVispWHaoLByT4VEyYdGTwTE4CTDz7pW6OkvqM0EHiBEIyBq8OkBqwmSZ9Vmq7NtCna5xcCwLCfOibbEXlah2qiay0T8WESVcGz8TSvqlnv+TNUVKeq7pJnUBI1cj2BVD9/ECk5k3tByIlH5RaujKzGvoABKo+ACsq6/oJc/wPtNH9OmFgj7ZWSgV0jZMfBAB7P/R9KoIAlWNW8kEpBk1fiHGPLMeVC1Zj3CPLMWj6wgbfL0E+QKoLG2T5maWiwsyvFxXmNzjJUDVuFZUelq3bhudWb8KyddvqvTfCPI/Cjimwd8KU7b8shapgLpd2aGHH2RaTz37pm6NsP1dNkDyBkIiZV8G4jLAXZ6W50T1OLizjRck8m25FBbiTRTWZlfapzJpKy/I39Homn/1J3RwlifQJhDQMXgVjwLZXHGalJmrXTIyTqWDL5LK/z0YrKv/3pe8uN3lAQnUqJ0w6xlnH0r7pZ79Lm6Piuv+DE4hgGLwKFoeATRXXZ6WmatdMjZPuYCvseEX90LfVikqVOB2QUJ3Uukdd9eU2nv0S7t+GxHn/h0sTCAkYvArmesCmksuzUpMbzVweJ5+NZX9fUlpRuXJAQs3XUTlhiprB07m0b+vZL3WSAMjfsKuCCxMIKRi8ChaHQEQVV2elpmvXXB0nn61l/+oktKICggVXcT4gQQcVGTydS/s2n/0SD8tJ0v4PyRMISRi8CuZ6IKKatA/AXNioW7Y5TlGzWTaX/auz/QGieyNV0g5IqE5VBk/n0j6f/ZmStv9D4gRCGqvB66xZszBr1ixs2LABANCnTx/ccsstGDFiBABgwoQJmDt3bsbvHH300Vi+fLnpS7XGxYBNJykfgLmyVbdsY5xUZLNsL/vXfC0bR6Ka2Ejl8gEJPhsb+qrTvbRv+9kvaWMU939QTVaD186dO+POO+/EgQceCACYO3cuRo8ejVWrVqFPnz4AgOHDh2POnDnp32natKmVa7XJtYBNN5dmpTbrlk2Ok6psloRlf5VMNPRP0gEJPlsb+qozsbRva6ylbYxK4v4PSZMHiawGr6eeemrG32+//XbMmjULy5cvTweveXl5KCoqsnF5orgUsNFeSahbVpnNisuyP2DumNmoBySECfJtZaUBuxv6qjO1tG/62S9xY1QSnqPVSZs8SCSm5rWiogJPP/00vvnmGwwcODD99UWLFqF9+/Zo3bo1Bg8ejNtvvx3t27fP+jrl5eUoLy9P/72srEzrdUvAGdpe0sZCYu2a6jFSmc2SuOzvCzJuJhv6J+GABJ+EDX3VScryqyB1Y5TE56guEicPElkPXtesWYOBAwfi+++/R8uWLfHss8+id++qIyhHjBiBM888E127dsX69etx8803Y+jQoVi5ciXy8vLqfL1p06ZhypQpJv8TrOIMbS+pYyHpA07HGKmuR5M0Xr6g42ayoX8SDkjwSdnQV52ELL8qkjdGSXwuqCZ18iBRyvM8q4fB7969G5999hm2b9+OZ555Bo8++igWL16cDmCrKy0tRdeuXbFgwQKMGTOmzterK/PapUsX7NixA61atdL232FDtg8A/5ZO0gzNhbGwnRXWNUbL1m3DuEca3kQ5/8IBgT7wdIyXymXq+sbtudWbcOWC1Q1ez31j+2F0v04Z1zdo+sIGg6ulE4fWum6bk7egWelB0xdmDZLq+28MO67V+f9/AnUH+hKeFdnofoaoGF/dbD9HddL1LHVFWVkZCgsLc4rXrGdemzZtmt6wdeSRR2LFihW477778Pvf/77WzxYXF6Nr1674+OOPs75eXl5e1qxsnHCGtpcrY2GzblnnGOmqR1M9XiaXqW009Hdlc0+U7F7cNvQFYWJy4sLGqDjv/2BXhdw1sn0BNXmel5E5rW7btm3YuHEjiotlPlxMCvIBEHcci4bpHCM/4AL2Blg+KfVofrat5hj4y9QlH5TW+Xthx80P6LP9F6dQFXjU19C/qDAzQCgqzG8wK+h/sI/u1wkDe7QNNeYVlR6WrduG51ZvwrJ121BRmX1xLsy4RvmAjjKu1Q3vW4ylE4di/oUDcN/Yfph/4QAsnThUdOAa5v4NStX4UjguTB6ksJp5nTRpEkaMGIEuXbpg586dWLBgARYtWoSSkhLs2rULkydPxhlnnIHi4mJs2LABkyZNQrt27XD66afbvGwROEPbi2PRMN1jZCqbZbq3Z9hxc7Whv+7DEYBoH9CSN/RVp3Jp2+TKUpI2RkmUtK4KUVgNXr/44gucc845KC0tRWFhIQ499FCUlJRg2LBh+O6777BmzRrMmzcP27dvR3FxMY4//ng89dRTKCgosHnZInCGtpfLY2GqfsvEGOkOuGz09owybhIa+us8YjbsuEb9gJa+7K96ed/0Jirp4+uLY+0rJw+5sxq8zp49O+v3mjVrhldeecXg1biFM7S9XB0LkxtsTI2RrmyWrd6eKgItF1pRmTocAVDzAS11h7+ONkc2Vpakjq9PamcZFVyZPNgmruaVcuNCnaEpLo6FqRo2n4tj5GsosAKqAqtstZkqlqmB8OOmog4V0FuLGqa2V0VWOkxdr0/VuPqCjG+2349yn2Zja2VJ9fiqYvrZaYNrNdk2WO82QOFxhraXS2NhqzuCrTGKurxnu7enhHtLYhbV5ax0TSoyebqW911dWdLBlc4yKsS5q4IKDF4dJ+kDwDZXxsJmI3DTY6QiKIi6bOr6MrWJWlQbhyP4r2H7A1rVUr+u5X3WQe4l+RAFMovBawxI+ACQwoWxsN0dwdQYqQoKpPT2VDluuWajpWdRJWSlfaY7UdSkc3nf9jhL2Rxl+9lpg5Sxl4bBK5FhLndHyJXKoEDVsqmUzHyQbLQLWVQJ42qjE0VNupf3XWifplsSnp3VSRp7abhhK+aibkKIC0njILERuOrxUXkogsrNZrY3+QTdbBIlixr0/rJ9OAIQ7j6MsoFHZSbPxKZI05uopG2Okvjs1EXa2EvDzGuMcdZWRdo4SKth0zE+qpf3bC+b1iXouIXJRicpi2ry+F6f6kyexPs0LImbo6Q9O3WROPbSpDzPi3UqrqysDIWFhdixYwdatWpl+3KMyVZv6N/mYfoNukjyOEgIqnWNz7J12zDukeUN/tz8CwcEqiOVUv8VZtzCjElFpYdB0xc2uBS9dOLQWuNg8/4K+v9T2Psw6n0WZXzrY+I+1f1v6HoPqyDh2amT5LHXKUi8xsxrDHHWVkX6ONiuFdQ5Prrq/3RsNgsaBIQdtzDZaBezqCYy0j4JnSiyva7OoMJE8CZ5c5TtZ6duksdeCta8xpDKekOXuTAONhuB6xwfVw5FKPmgFIOmL8S4R5bjygWrMe6R5Rg0fWG99WRhxy3sErXtWlSdhyMA0e5DlZ0oohyYYJKpWkjpm6OkHqKggvSxl4CZ1xjirK0Kx6F+usdHev1f2FZeYcctSjbahSyqyYy0L26dKBpicjWJhyPYw7FvGIPXGOKsrYrL42CiZs7E+JgICkz39ww7blGXqE33MDZxOAKg5vheFcv+usZX5XvZZJN+VzZHSamDV8mVsbeJwWsMcdZWxdVxMLUZwdT46Ay6bPT3jJpBdaHZvKnDEYB4HN+bjer3sunVJMljC8R745b0sbeNwWsMcdZWxcVxUHUqVS5cHJ/qooxVlCAg6ri5UAJg6nAEQObxvSqyeTreyzZWk6SWVJh8Vtoidewl4IatmHJtE4IuLo1DQ9kuoCrbpfKABVvjE/VQhKhjFTUIiDpuppv6Sz4cAVBzH6oa0zCb+GrS9V621aRf2uYoG89KW6SNvRTMvMYYZ21VXBkHk/Vs1ZkeHxVLfVHHSkXJhO37KtdxdOFwBMD+eALqsnm63suur5aoYutZSXIweI0505s8pHJhHGx2RzA1PqqCAyn9PVWNm6qm/nWNY5gP+rDBfdQ6PVvj6f+Oqp38Ot/LtmshJWyQYicZYvBKJITL3RFyoTI4UNnf0/aGCN1N/ZNyOILPxia+mnS/l12om9Yp7s/KukiYNEjC4DXB+GaQNQaudkfIlcrgIC79PcNkooOOY9TDEcIE9y5kpGtSmc0z8V6W3jpNp7g/K2uSMmmQhMFrQvHNIG8MJNazqQzuVQYHkvt76mxHBQQfRxcPRwDMHjMLqM3mSXwvRyHtqO24jW99JE0aJGG3gQQydbygZFLHQFJ3BBW7rqtTvdQnaax8QcbM1DGzUY/qtbHb2fQxs4D6nfwS78+wJB61HafxzSZJXRWCYuY1YaTNoG2QPga2l7IBPbN9HUt9usYqTMY56JiZbOpvu743yHjaOGYW0JPNM/Ve1l3+JHWDlIRnpU7sqpAdg9eE4ZvBjTGw2R1BV3Cva6lP9ViFKScx1Y4KCD+OrmzysXHMrE9HkK/7vWyi/EnyBikXOsmEJXXSIAHLBhKGbwaOQUN0LhFKX+oLW04SZsxsNPVXUQIQ5ICJMOMZNSMdddl/eN9iLJ04FPMvHID7xvbD/AsHYOnEodbvzbqYKn+ydThC0kmeNNjGzGvC8M3g9hiY6I6gO7g3kQE03efTdDsqwE4mNUiWL+x42jxmtvpr6cjmqXz/mix/StIGKUmS1lUhCAavCcM3g7tjYKo7gongXudSn40+nzbaUQHRxzFIMBW0pjfseEbtjiChd29dVL9/TZc/SR5bn6TWhypw0pAdg9eE4ZvBzTEw2S7F1eAesNfn08V2VLqzqGHH08WMdEN0vH9tlD9JHFuftNaHqrgwabCBNa8JJL3u0ASXxsB0u5SorZVsiTpOUTLOUtpR5VqPGrRWMkxNb5TxjPr+VN3eK0idb12/q+P9a6v8yUbrtIZIbX2oikt12KYw85pQkmfQprgyBja6I9iY7Udd8os6TlEzzrYzJLlmnkxlUVWMp4T3Z9SMnq73r8srJCpJb32oSpy7KoTB4DXB+GZwYwxsdUcwGTyoWPKT0OfTZglArsvSYYKpMFk+FeNp+/2pYrlf1/vXxfInHVxofUjqsWyASDib3RFMLBGqWvJT2eczSjmJ6RKAoMvSUbKoQVslSSnPCbPsr2q5X+f71+b4RimlUClJrQ+ljLkEzLxSneK2a9NlcV4eVLnkp2qcJCxXB8lEB808mc6i2h5PG90nqtP9/pXeMk03l1sfBiFpzCVg5pVqUX2mvYskzXClbaBSOTYqD0RQOU4qM85BxytoJjpo5slGFtX04Qi+KFl9VRk9E+9fk5uopG2OSsIBCtLGXAJmXimDyZZMUkmc4dreDORTPTaql/ykjJMv6HiZOGbWxSyqqSN7q1OZ0ZN2X4YlcXNU3Gt/JY65BAxeKY1vEtnBu4TlV9Vjo2PJz/Y4+cKMV5il6jDL0lGCKRWbqHQejuCz3X2iJlP3pc6SL6mbo+IyOaiL1DG3jcErpSX9TeJC8G5r97WusdFVD6hjnIIEBWHHy+Qxsy5kUU0f2Vudjoye7vev7lUjyZujpExaVZM85jax5pXSkv4mUVl/GTe6xkZaPW82QevAw45X1GNmg9ajmq5FNXE4gk9K9wlTTNRFSt8cJfEAhaikj7ktzLxSWtLfJC4H77q7Q+gcG+lLfmGWrcOOl2vHzEo9YhaIV/eJhphaNYpz5xOpOOZ1Y/BKaUl/k7gavJvYYKZ7bEwECGEC/LBBQdjxirpUbbKsJGhQb+pwBJ/KZX/bZSgNMVXy5crmqDi1enRlzE1j8EppSX+TuBi8m9pgZmJsdAZepnt9Rs2g2sxE5/LB79IRsxKz+tK7dtRH6pj6JHaLiUr6mNvA4JUyJPlN4lrwbnKDmWtjU12UAD9sUBB1vKRvpnLpiFlpy/6udO2oj7Qx9UnuFhOV1DG3hcEr1ZLkN4lLwbvp7hA2xibq8p/NXp9Rx0tySyqTWVQV953KrH6Ue9K1rh31sdX5JBsXusVEJW3MbWLwSnVK8pvEleDdxgYzk2OjYvnPdq9Pm/eSzs1USTti1hf1ntQ14XR5ZUSVpLd6TBq2yiKqgwstV2xtMDMxNqra/qjq9QmEb+WlarwktaRy9YhZIPzxxiruSRNdO2y09ZJwnLbL3WKCkjDetjHzSoHEaRen61zcYJYLlct/Knt92iwlkdaSytUsatjMqap7Mg5dO2qSskHK1W4xQUkZb9sYvFLO+KaRFbxLWiqU2vZHaq9PncejmtpMZfuI2aCibOZRdU+63rWjJkkbpOI6ma9O0njbxuCVcsI3jczg3bWsYC5ULv9J7PUpLYsKRNtMZSuLauK4Xp+qe1LShDMqaRuk4jS2dZE23raFrnldt24dbrrpJowbNw5bt24FAJSUlOBf//qXsosjGRp60wBVb5o4192YOHoxrOF9i7F04lDMv3AA7hvbD/MvHIClE4caC1xVj4vq5T9JR3yaOB41ymYqIHhtr436cFPH9fpU3pOS7scoJB6nHZexrYvE8bYpVOZ18eLFGDFiBI499lgsWbIEt99+O9q3b4/3338fjz76KP785z+rvk6yKOm7OF2Y8dpYhnWp7Y+ODGHQUgkXsqi2svg6yyiA6JlT1fekqYy1zjInqRukpHSmUE3qeNsSKni9/vrrMXXqVFx99dUoKChIf/3444/Hfffdp+ziSIakv2mSHrxn41rbH5UBfphSCRca+0vf8GP6uF6fjntS94RTd5mT5A1ScWz1KHm8bQhVNrBmzRqcfvrptb6+3377Ydu2bZEvimRJ+pvG1eBddzuVuLb9aUjYUokoWVSXWlJJaunlCzuO1Um+J2syUeakYkwpdxzvTKEyr61bt0ZpaSm6d++e8fVVq1ahU6dOSi6M5EjCLs76uBi8m9hc5nrbnzBLqlFKJZhFtbMZDVCXOdU1piqX902VObmwQUpSd5ioXBhvk0IFr2eddRYmTpyIp59+GqlUCpWVlfjHP/6Ba6+9Fueee67qayTLkv6mcS14N9UZwuW2P2GD+yilEjZqUU0eMSu1pZdPVU2v6ntS9UTTZJmThG4n2UjsDhOV5PE2LVTwevvtt2PChAno1KkTPM9D7969UVFRgbPOOgs33XST6mskAZL8pnEpeDe5ucylcakuSnAfpVTCtSwqkHsAIH0zmk/aZh4dE03TZU7SxhSId2tHieNtQ6jgtUmTJnjiiSdw2223YdWqVaisrMThhx+Onj17qr4+EiTJbxpXgnfTm8tsjEuUpcCowX3UUglXsqhAsADAhc1o1V9DZeY07P2oa6Jpo8xJ0gYpF7rDRCVpvG2JdEhBjx490KNHD1XXQg5I8pvGheDdxuYyk+MSdSkwanCvolRCehYVCB4AJKWlV01R7kddE03XypxUY3eYZMg5eL366qtzftEZM2aEuhgi6aQH77Y2l5kYFxVLgVGDe1WlEqbvI931qEnYjFZT1PtR10TT1XIeVVztDkPB5By8rlq1KuPvK1euREVFBQ466CAAwH//93+jcePGOOKII9ReITkhTrs6XRbXrIuqpUAVwb2kzF8u7zsT9aiubkYDzHed8OmcaLpy4IQOLnaHCcv2WNuUc/D6xhtvpP/3jBkzUFBQgLlz52LfffcFAHz99dc477zz8OMf/1j9VZJocdzVGZSUh4i0rIuqcVG1FKgquJeS+cvlfWeiHtXVLKqNrhM+3RNN6a3SdInrBL4mCWNtU8rzvMCdyzt16oRXX30Vffr0yfj6Bx98gJNOOgmbN29WdoFRlZWVobCwEDt27ECrVq1sX07sZFs68x+PLu/qzJXEh4iEa1J5Dc+t3oQrF6xu8OfuG9sPo/vV32vav2eBuoMsG/ds0CA/yPsuzNhVVHoYNH1hgwHA0olDM67T5n2ncwxrUnU/SrwXw5L0WRCnca2LpLFWKUi8FmrDVllZGb744otawevWrVuxc+fOMC9JDkrCrs6GSG3JYjszqHpcVC4FSlr2B4IHfEHfdybrUV3YjAbY7zrhk3YvhiXtsyAu41oXaWNtS6jg9fTTT8d5552He++9FwMGDAAALF++HL/61a8wZswYpRdIciV9V6f0h4itzWU6xkX1UqDt4N4XJsgP+r4zXY8quaWXT0LXCZ+pe1FnaZPEzwIp73HVJI61DaGC14ceegjXXnstfvrTn+KHH36oeqF99sHPfvYz3H333UovkORK+q5OPkTqpmNcdNTy6gjugwQIYYP8oO871+pRdR8xC8jpOlH99XQ+I3SXc0j9LJDeHSYMqWNtWqMwv9S8eXM8+OCD2LZtG1atWoV3330XX331FR588EG0aNFC9TWSUEna1VkXPkTqpmtc/ExgUWHm/VRUmC+ixqvkg1IMmr4Q4x5ZjisXrMa4R5Zj0PSFKPmgtM6fDxLkVxfmfRdl7PwAYHS/ThjYo632wPXiP75ba1z8LGrNsTQ5hjVJvx99Qcc0jKR/FpjEsa4S6ZCCFi1a4NBDDw39+7NmzcKsWbOwYcMGAECfPn1wyy23YMSIEQAAz/MwZcoUPPzww/j6669x9NFH43e/+12tWluyIym7OrNx8SFioiuC7hZAOjOBYccnzNJ12CA/ShmAjWXUXMfU1BGzQLy6TtTHVGlT0j8LTOJYVwkVvB5//PFIpbLf6AsXLszpdTp37ow777wTBx54IABg7ty5GD16NFatWoU+ffrgrrvuwowZM/D444+jV69emDp1KoYNG4a1a9eioKAgzKWTQtLaMpnm2kPE1E5w3eOiaykw7PiEDRDCBvlR3nc2DkfIdUxNHTELqH122S4/qY+p0iYXPguktDOMyoWxNiFU2UC/fv1w2GGHpf/07t0bu3fvxrvvvov/83/+T86vc+qpp+KUU05Br1690KtXL9x+++1o2bIlli9fDs/zMHPmTNx4440YM2YM+vbti7lz5+Lbb7/Fk08+mfU1y8vLUVZWlvGH9HFl6UwH/yEC7H1o+KQ9REwsHfpcGhdflPEJu3TtB/nZRiGFqkCvriDf5vuuotLDsnXb8NzqTVi2bhsqKuvuthh0TKMcMevaGNYnaPlJfUyWNkkdT0DtmEogeaxNCdXnNZvJkydj165duOeeewL/bkVFBZ5++mmMHz8eq1atQn5+Pnr06IF3330Xhx9+ePrnRo8ejdatW2Pu3LlZr2HKlCm1vs4+r3rFZVYbhoSeqvXxe3ZmC7Cy9eyMSvq4+KKOT5Sen1H7UZp+3+X6/2mYMV22bhvGPbK8wWuYf+GAjCyha2NYH9X9O8OOaRSSxhOIb09UQN5YRxWkz6vS4PWTTz7BUUcdha+++qrhH/5fa9aswcCBA/H999+jZcuWePLJJ3HKKafgrbfewrHHHotNmzahY8eO6Z+/6KKL8Omnn+KVV16p8/XKy8tRXl6e/ntZWRm6dOnC4JW0kvwQsfEB5jM1LlH+najjE/X3XWnsHyQICDMmYQ9G8K9NwkQpyn2oY5IZZUzjwNbEncLRfkhBNsuWLUN+frBNGAcddBBWr16N7du345lnnsH48eOxePHi9Pdr1tZ6nldvvW1eXh7y8vKCXThRRJJbstjsimBiXKIGLlHHJ2qNrwuN/YPW9YYZU9daetUU9T50pcWcS9jOML5CBa81DyLwPA+lpaV45513cPPNNwd6raZNm6Y3bB155JFYsWIF7rvvPkycOBEAsGXLFhQX733jb926FR06dAhz2WSJ5KxkErjYFSFXKk7yijo+KgIEVUF+ru+1oOMWNAgIO6ZRTkYyfThCdSruQ90t5uJ42lRDktLOMImfsaGC11atWmVkPxs1aoSDDjoIt912G0466aRIF+R5HsrLy9G9e3cUFRXhtddeS9e87t69G4sXL8b06dMj/RtkjpTlPFskPFRc64qQK1VtgFSMj4QAIUg9qu6WVFHG1IVMdHWq7kOXW8xlY/v5F+eJuy+pn7GhgtfHH39cyT8+adIkjBgxAl26dMHOnTuxYMECLFq0CCUlJUilUrjqqqtwxx13oGfPnujZsyfuuOMONG/eHGeddZaSf5/0Un2+vWukPFSkLR1KawOkanxUBwi6jkg10ZIq6pjaaOkV9lml6j50tcVcNhKef3GduPuS/BkbqlXWAQccgG3bttX6+vbt23HAAQfk/DpffPEFzjnnHBx00EE44YQT8Pbbb6OkpATDhg0DAFx33XW46qqrcMkll+DII4/Epk2b8Oqrr7LHqwMaykYAVdmIbG12XGeyNVUupLRWkdoGSNX4qDqNKsg4BX2vmWpJ5UI7L/9nozyrVN2HLraYy0bK8y9OY1pT0j9jQ2VeN2zYgIqKilpfLy8vx6ZNm3J+ndmzZ9f7/VQqhcmTJ2Py5MlBL5EsS3KhvKlTbYKyvalFdZZA9ZKg7fHxSaxHDZtJtTGmQTN+UZ9VKu9DCeUnUUl7/sVhTOuS5M9YIGDw+vzzz6f/9yuvvILCwsL03ysqKvD3v/8d3bp1U3Zx5K6kFMrXRfJDxVZXBB0faDqWBFWPT9ASCcn1qGGDgKhjqqt8wme720RNJgJ+nbWoEp9/UiamKiX5MxYIGLyedtppAKoyouPHj8/4XpMmTdCtWzfce++9yi6O3JWEQvlskv5QqUsS2wCFqfmTXo9qOgjQ2c7LJ6HbRF2vqSuw012LKvX5J7mdYRhJ/owFAta8VlZWorKyEvvvvz+2bt2a/ntlZSXKy8uxdu1ajBo1Ste1kkOiHNvoOhcfKkFqBMPQ3QbIdi1vTWFr/lyoR41a16vreFkbx/T6pN6HNZmoRXXx+eeiJH/GAiFrXtevX6/6OihmpGfFdHJth6uJXcEutwEysfTvYz1qFRPlEz6p3SZ8qpb4TdWiSn/+2W7fpUqSP2OBAMHrb3/7W1x00UXIz8/Hb3/723p/9oorroh8YeS+uBbKN8Slh4qpViuutgEytfTvc60eFdBzMIKJ8onqVD2rVN+HKieWpmpRJT//JLTvUimpn7EAkPI8L6f1we7du+Odd95B27Zt0b179+wvmErhf/7nf5RdYFRBzsolPeIy0w1K+oPS9LnffvAC1P2BJml5FcgebDV0vc+t3oQrF6xu8PXvG9sPo/t1yvrvAsHHyfR7LUgmNci9FmYM/X+jocC/vvtZ0rMq7P2XTdT7Mihpzz/V4ymJpPs2iiDxWs6Z1+qlAiwboCDiViifK+k7XE3vCjadJYjyQDe99F+dzSNSpRyMYLJ8ojqVzypb9182pmtRJT3/pLXvUi2Jn7Ghal5vu+02XHvttWjevHnG17/77jvcfffduOWWW5RcHMVbXGaL9ZH8ULGxK9jUB1rUrI+Npf/qJNejAsGDAentvFSzef9lY6MWVcrzT2L7Loom1AlbU6ZMwa5du2p9/dtvv8WUKVMiXxTFn8qTligcW7uCVZ1ClY2KHdVRAntVp/roHqfqdO/sD9vOCwg+hsP7FmPpxKGYf+EA3De2H+ZfOABLJw41GrjavP+yifNpUw2R2r6LwgsVvHqeh1Sq9g3+3nvvoU0bGTuoSS4pRwfaorstVa7i2GpF1ZGJqpb+bbdOyuVeCzNmYTOprrTz8gV9r0q5/7KxdV/afuaxfVf8BCob2HfffZFKpZBKpdCrV6+MALaiogK7du3CL37xC+UXSfER99qjhkjaxCBpV7CqEhJVy4OuLv1Xl+u9JvlgBJtjaLrTRHU6l/glHzShi/T2XSokoQyvukDB68yZM+F5Hs4//3xMmTIl43jYpk2bolu3bhg4cKDyi6T4SHLtkam2VEFIqBFU+eGmanlQVWBvawNQkHstysEIQYIBF9p5+cK+V6Xdf/W9vonnq5RnnqSJug4SJgimBQpe/SNhu3fvjmOOOQZNmjTRclEUX0mtPZKccbad3VL54aZyeVBCYO/TuZmKByNkstlpojpJ918Y0p55ro9nNlImCKaF6jYwePDg9P/+7rvv8MMPP2R8n/1UKZuk1h5Jzzjb2BWs48NN9fKgjmBLdxYw6L3m2sEIujLQPtudJqozEezrWm6W+MyzXcqjmrQJgkmhgtdvv/0W1113Hf70pz9h27Zttb5fUVER+cIonpJQe1SXpGac66Pjw03H8qDKwN5EFjDovRZlzCTXT4b9YFfRaULq/VeTzuVmqc88Ke27VJA4QTAlVLeBX/3qV1i4cCEefPBB5OXl4dFHH8WUKVPQsWNHzJs3T/U1UowktV2Lixln3TuEdX24SdnpX1OYLhtBW1IB4e41CTv7G6K7nZcvLp0mGqK764uLzzzXSJ0gmBAq8/q3v/0N8+bNw5AhQ3D++efjxz/+MQ488EB07doVTzzxBM4++2zV10kxEtfao/q4lnE2sQFA54ebzoxgmGVWk1nAKGUANpZUcxlPExloXxw6TTTExHKza888FyV5ghAqeP3qq6/QvXt3AFX1rV99VTVzHTRoEC6++GJ1V0exJf3hrppLu11NbQDQ/eGmY3kwbFAfdnnP9DGpppdUJbXz8knsNOGT1lKuPi4981yV5AlCqLKBAw44ABs2bAAA9O7dG3/6058AVGVkq7fPIqqPyROEJHBhOVFVk/VcuFZCEmWZNWoWMOhBEjbvtVzLTYKMZ5QMdJhDOCS+V1WeSmhquVniOPpsH5yggmvPUJVCZV7PO+88vPfeexg8eDBuuOEGjBw5Evfffz/27NmDGTNmqL5GotiQnnE2vQHAlRKSqMusNrKAkttSSW7n5ZP0XpXcUq4hksbRF6e+qK48Q1VLeZ4Xebrx2Wef4Z133sF+++2HOXPm4LHHHlNxbUqUlZWhsLAQO3bsYAsvRyTtpBBJnlu9CVcuWN3gz903th9G9+uk7N818f95lH9j2bptGPfI8gZ/bv6FA+oM6isqPQyavrDB5b2lE4fWeU22PmxVtKXyf7p6gBV0PKOMn4RAJcq95/+3Z5tUNnTv1PeaYe9HlwW5T10Sh8/NIPFaqMxrTfvvvz/2339/vPfee5g7d66o4JXcIuGDJslsbQDQXWsZ9b6KuszqYhZQZ1uqOLfzqinqvedKSzkXxLkvapxagOUiVM0rkQ66W7dIJ6EGK0qdoFQq7isVQX3U+j8VNeI66lGB4G2pXGznFeb9qeLeS1pLOZ3Ctk8jeZRkXomiivOMOBdSMs5xy8iouq9U7eq1fRSvjnpUIHiA5Vo7rzDvT1X3nqst5SRKcl/UuGHmlURI8oxYWsZZQkZGVRZa1X2lclevqi4bQcYoyD1m4mCEKONpuktJ2PenqntP92qI6fG0ucKU5L6ocRMo8zpmzJh6v799+/Yo10IJltQZsdSMswsZwlyovK8k7eqVVI8KhMuk2hrPIBtborw/Vd17cVoNsb3ClOS+qHETKHhtqIdrYWEhzj333EgXRMmU1Bmx5LOpbWwAkN4SSMIya9AxCnqPmWxLZXo8gwZPUd6fKu89SROnsEwdflKfOE0Eki5Q8Dpnzhxd10EJl9QZcVIzznXRkYXWcV+pDup1ZwJN1qOGCbCijmeu4xcmeIry/lR970mYOIUlaYUpDhMB4oYtEiKpM+KkZpzrksSWQCYygWHrUV1oS6VzExoQ7f2p497TuRqis0+otBUmlycCVIUbtkgMCRuFTHOtNZXOzRZJawkUZiOQqWNSbbWlkrQJDYj+/pR679Wk8ujZukhcYUra8eRxw8wriZK0GbH0zGB1ujdbuNwSKGjWymQmMI71qCY2oQFq3p/Sn2kmalG5wkSqMXglcZJ2UogLNVgmPuB01z3ruq/CBPVhl1FZj1rFxCY0n4r3p9Rnmqla1KTuaSB9GLwSCSA5O2PqA86lLLQvbFBvIxMoNZMqeROaT+L7U0WNqqlaVBff2yQbg1dygs7NBFJIzc6Y3GxhOgsd5b6KEtTbygRKzKRK34Tmk/T+VFXCY7IW1YUVJnIHg1cSz3Zj66QzvdnCVJYr6n0VJah3MROoK5Ma90MRVFNZwmO6FlViBpvcxOCVRJPQ2NomCRlnG5stdGe5VNxXUYJ61zKBOjOpcd6Elk3Y97XqEh4btaiSMtjkLgavJJakxtY2SMk4x22zhar7KmpQLyETmEsQpTuT6tomtKiivK9Vl/CwFpVcxeCVxJLW2NokSRlnKR9wqrLQqu4rFUG9zUxgrkGU7kyqS5vQqgtzP0Z9X+so4ZEwiSIKisEriSWxsbUJEjPOtj/gVGahVd1XqoJ6VZnAIMFUkCDKRCbV5ia0MMLcjyre17pKeKSUUxDlisEriZXUxtZSM862PuBUZ6FV3le2g3qfzgb/pjKprgRQYe9HFe9rnSU8tsspiIJg8Epixa3WMleSM86mP+B0ZKFV31eqg66gy9G6G/ybzKTaCKCCjHeU+1HF+1pKCQ+RbQxeSaykPqiTmnGui44stI77SlXQFXQ52kSD/zhnUoOOd5T7UdX7Wkq2n8gmBq8kWhIf1K5lnHW289KVhZZ4X4VZjjbR4B9wJ5Oqq+7XF+V+VPm+dmFiQKQTg1cSL2kPapcyzrrbeenMQuu8r4IG9GGXo001+Afkvw911v36otyPqt/XrFGlJGPwSk5I2oNaYmawJhPtvHRnoXXcV2EC+rDL0SYb/Pu/KzGTqrvu16fiZDTp72siFzB4JRJKcqbLVDsvl7LQQPiAPuxytOkG/ybpOo4WCD/eKu5Hye9rIlcweCWnSTg+VSepGWeT7bxMB1o2ju4MuxztYoP/XMZX53G0QLTlfxX3o9T3NZErGLySs6Qcn5pEptt5mQq0bB3dGWU52qUG/7mMr+7jaAE1y//MnhLZw+CVnCTp+NQkstHOS3egZfPozqjL0S4ck5rr+Oo+jhZQs/zP7CmRPQxeyTkSj081SUKphGvtvBoi4ejOqMvRko9JDTK+Jo6jBdyo+yWiujF4JedIPT7VBCmlElI2UqkK5KUc3enScrSuulRTx9ECbo03Ee3F4JWcI/n4VJ2klUrYzlypDOQlHd1pezk6lwmBzrrUUYd2NHYcLWB/vIkoOAav5JwkHp8qtVTCVuZKdSDPozur5Doh0FmXGufjaIlIDQav5Jy41VvmQnKphI3G9aoD+bge3anruFTddamuHEdLRHYweCXnSKm3NCmppRJ10RHIx/HoTp3HpZqoS5U0CSAiWRrZvgCiMPzMTFFh5odoUWF+LNtkuVYqUVHpYdm6bXhu9SYsW7cNFZV1hUXh6ArkJd9TQcfTz6LWDPL9LGrJB6UZXw8yIQD2ZlKzhZEpVAXKddWlBhlffxIwul8nDOzRloErEQFg5pUclqTMjEulEro7IugM5CXeU0HH08RxqaxLJSKbmHklpyUlM+MHCwBqZbsklUoEzfiFESbrF4SkeyrMeAbNogLhJgRhM9WSxpeI3MTMK5EjpO9kN9URwdWa56A9acOOp8njUplJJSIbGLxSLEk4hUoHycGCyY4I0gP5msKUUoQdT9PHpUrYnEZEycLglWJHyilUukgNFkx3RJAcyFcXtidt2PHkcalEFHcMXilWpJ1ClSQ2OiKYCuTDZvKjlFKEHU8el0pEccfglWJD6ilUJkgok3CpI0IQUTL5UUopoownj0slojhj8EqxIfkUKp2klEm4upGqPlEz+VFKKaKOJ7OoRBRXVltlTZs2Df3790dBQQHat2+P0047DWvXrs34mQkTJiCVSmX8GTBggKUrJsmSeAqVidZUQUhu9B9UQ5l8oCqTX9+BAVFLKaKOJ9tSEVEcWc28Ll68GJdeein69++PPXv24MYbb8RJJ52EDz/8EC1atEj/3PDhwzFnzpz035s2bWrjckk4106hikpqmYTtjJ+qEgoVmXwVpRS2x5OISBqrwWtJSUnG3+fMmYP27dtj5cqVOO6449Jfz8vLQ1FRUU6vWV5ejvLy8vTfy8rK1FwsiRfXmstsJJdJ2KqbVFlCoSKTr6qUgnWoRER7iTpha8eOHQCANm0yg4tFixahffv26NWrFy688EJs3bo162tMmzYNhYWF6T9dunTRes0khyunUKmSxDKJ+qguoVCVyY9TKQURkQQpz/OyF2wZ5HkeRo8eja+//hpvvvlm+utPPfUUWrZsia5du2L9+vW4+eabsWfPHqxcuRJ5eXm1XqeuzGuXLl2wY8cOtGrVysh/C9klZQOTbsvWbcO4R5Y3+HPzLxwQ+6xdRaWHQdMXZs1E+1n3pROH5jx58V+zoUx+rq8poSMEEbklSc+NsrIyFBYW5hSviek2cNlll+H999/H0qVLM77+k5/8JP2/+/btiyOPPBJdu3bFiy++iDFjxtR6nby8vDqDWkqOpNQIJq1Moj46SihUd0/g0j8RBZGUREwYIsoGLr/8cjz//PN444030Llz53p/tri4GF27dsXHH39s6OrIRUnYZe1imURFpYdl67bhudWbsGzdtnp36gehq4SCS/5EZIO0TjLSWM28ep6Hyy+/HM8++ywWLVqE7t27N/g727Ztw8aNG1FczA8NIpeO9NSZRdDZaSIpmXwikkFqJxlJrAavl156KZ588kk899xzKCgowJYtWwAAhYWFaNasGXbt2oXJkyfjjDPOQHFxMTZs2IBJkyahXbt2OP30021eOjkorrVDLgRXuo/t1V1CwSV/IjJFcicZKawGr7NmzQIADBkyJOPrc+bMwYQJE9C4cWOsWbMG8+bNw/bt21FcXIzjjz8eTz31FAoKCixcMbkq7rVDkoMrE1mEOJ7uRUTquZDEYCeZhlkvG6hPs2bN8Morrxi6Goor3Vk/qp+pLIJLJRREZJ4rSYykHbgThphuA0Q6sHbIPpNZBBdKKIjIPJeSGOwk0zAR3QaIdAmS9SM9TGcRktBpgohy11ASA6hKYqjqfhKVi51kTGPwSrHG2iF97aly5WcRsj1mU6hauktyFoEoaUw+l1xMYrBNX/1YNkCxlvTaIQk1XtxMRUTVmX4uuZrEYBlUdsy8UqwlOesnqck1swhEBNh5LrmcxGAZVN2YeaVYS2rWT+JGNWYRiNyiuq2UrecSN0DFD4NXir0ktlCS2uRacj9aItpLx9K+redSUpMYccbglRIhaVk/V2u8iMg+XW2lbD6XkpjEiDMGr5QYScr6uVzjRUTRRFnu17m0b/u5lLQkRpwxeCWKIdZ4ESVT1OV+nUv7Ep5LSUpixBm7DRDFEJtcE7krbA9UFTv5dS7t87lEqjDzShRTrPEick/YzKmq5X7dS/t8LpEKDF6J/pfqtjASsMaLyB1RNkqpWu43sbTP5xJFxeCVCDJOotKFNV5EdgSZEEfNnKpa7jfVVorPJYqCwSslnq62MESUXEEnxFEzpyqX+7m0T9IxeKVEk3gSFRHJlGsmNcyEOGrmVPVyP5f2STIGr5RoUk+iIiJZcs2khp0QR82c6lju59I+ScVWWZRoPImKKLlybUkVpAVVkAlxdX7mNFtomUJVsFxf5tRf7i8qzAxwiwrzWf5EscLMKyWa7RNfiMgOXZnUsBNiVZlTLvdTEjDzSommIttBRHLkkk3VmUmNMiFWlTn1l/tH9+uEgT3aMnCl2GHmlRLNVFsYItIvl2yq7kxq1I1TzJwSNYyZV0o81okRyaYym6o7k6riCFRmTonqx8wrEZjtIDItSNspldlUE5lU9kkl0ovBK9H/YlsYIjNy3SyVa7/UINnUsJnUoKVFnBAT6cOyASIiMibX5f2GsqlAVTa1otILlE0Ns0kzbGkRl/+J9GDmlYiIlGioFCDI8r6ubCozqUTuY/BKRESR5VIKECQgDZJNHXVox0B1qWFrUllaRCQDg1ciIsoql41VudamBglIdWdTmUklcheDVyIiqpPqnf5BAtKgu/zDZFOZSSVyE4NXIqKEyLU9FaBnp3+QgJTZVCLKhsErEVEC5NqeCtDXNzVoQMpsKhHVhcErEVHM5ZpF9ensmxo0IGU2lYhqYvBKROSgXEsAgmRR/d/XudMfCB6QMptKRNUxeCUickyQEoAgWVQ/QDTRN5UBKRGFxRO2iIgckusJVb4gWVRf0FOowp5ARUQUBjOvREQWBNn5X/13gpYABK1JBcCd/kQkGoNXIiLDgiz7VxemBCBov1Qfd/oTkVQMXomIDAq687+6MCUAYWtSAWZTiUgm1rwSERnS0LI/ULXsX1FZ10+EKwEAotWk+tnU0f06YWCPtgxcicg6Zl6JiHIUpk61ujDL/tWFLQEAmEUlooZFfcaZwuCViCgHYetUqwuz7F9dlBIA//dZk0pEdVHxjDOFZQNERA0I2p4qm7DL/tWxLRURqabqGWcKM69ERPUI054qmyjL/tWxBIBIDleW2rNR+YwzhcErEVE9otapVhd12b/ma7EEgMgul5bas1H5jDOFZQNEFDsVlR6WrduG51ZvwrJ127Lu3s9F1DrVmrjsTxQPri21Z6P6GWcCM69EFCuqMyEq6lRr4rI/kRq2luxdXGrPRsczTjcGr0QUG1EOAMhGVZ1qTVz2J4rG5pK9i0vt2eh6xunEsgEiioWoBwBk49epAnvrUn1B61SJkkRl+U5NtpfsXVxqz8bFZxyDVyKKhSCZkKBYp0oUTMkHpRg0fSHGPbIcVy5YjXGPLMeg6QuVBJW6JqpBuLjUXh/XnnEsGyCiWNCdCWGdKlFudJTvVCdhyd7FpfaGuPSMY/BKRLFgIhPCOlWKu6gboExsZJKwZK+y7Z0krjzjGLwSkRamdwHHMRNCZJKKDVAmsqJSluz9pfaaY1bkWJ9XFzF4JSLlbOwCjmsmhChXUSaMqpb6TWRFJU1UXVpqD0L6qWEMXolIKd31bvVhJoSSKsqEUeVSv6nyHUkTVVeW2nPlwqlhKc/z9G3HE6CsrAyFhYXYsWMHWrVqZftyiGKtotLDoOkLsy4b+hmRpROHav1gkZ41IKpP0Ps324TR/42GJozL1m3DuEeWN3hd8y8c0GCQ5j8DGsqKqngGuBBkuSbqvRRFkHiNmVciUkbCLmAgfpkQSo6gAZmKrKnKpX6TWVGJS/YuT5xdOjWMwSsRKSNhFzCRFKoyqPWV3KiYMKpe6jdZviNpoup6JlhK8iEXDF6JSBkpu4CJbDOVQVUxYdSxAUpiVlQnm7X+qriUfOAJW0SkjP8hmO3jKYWqD3C2qyIX5XrcaZijS8OeEKdiwqjreFA/Kzq6XycM7NFWe+Cq8zjahv5d2yd+qeBS8oGZVyJSRtouYCJVcs2kms6gqsqaut6pw+aSvUvL7fWR1IKsIcy8EpFSrp2RTcmWS7YuSCbVdAZVZdZ0eN9iLJ04FPMvHID7xvbD/AsHYOnEoeLfs2Ey3Sq5tNxeH10ZeB2YeSUi5ZJW70ZuyiVbFzSTaiODqjJrqmsDlK5d+BJ2yLu03N4QVzLwDF6JHOBi+xVJu4ApOXJ9r+S6wSboknDUDGrYkhvJE0adS/oSluxdWm7PheR7yWc1eJ02bRr+8pe/4N///jeaNWuGY445BtOnT8dBBx2U/hnP8zBlyhQ8/PDD+Prrr3H00Ufjd7/7Hfr06WPxyonMcb39CpEpOupSg2ZSbWZQVU8YVUyade/Cl7BkH8daf+nJB6vB6+LFi3HppZeif//+2LNnD2688UacdNJJ+PDDD9GiRQsAwF133YUZM2bg8ccfR69evTB16lQMGzYMa9euRUFBgc3LJ9IuDu1XiKLKJYgK8l4Jkq0LmkmNSwZVxaTZxJK+lCV7V5bb48Jq8FpSUpLx9zlz5qB9+/ZYuXIljjvuOHieh5kzZ+LGG2/EmDFjAABz585Fhw4d8OSTT+LnP/+5jcsmMkJCLReRbbbrUkcd2jFwJlVaBjUoVZNmE0v6kpbspUw8kkBUzeuOHTsAAG3aVN1k69evx5YtW3DSSSelfyYvLw+DBw/GW2+9VWfwWl5ejvLy8vTfy8rKNF81kR4SarmIdHCpLjVsJtV2IBN2yV/lpNnEkr60JXvbE4+kEBO8ep6Hq6++GoMGDULfvn0BAFu2bAEAdOjQIeNnO3TogE8//bTO15k2bRqmTJmi92KJDJBQy0Wkmot1qWEzqbYCmShL/ionzaaW9LlknzxigtfLLrsM77//PpYuXVrre6lU5ozJ87xaX/PdcMMNuPrqq9N/LysrQ5cuXdReLJEBUmq5iFRxuS7VViY1aAY16pK/ykmzySV925luMktE8Hr55Zfj+eefx5IlS9C5c+f014uKigBUZWCLi/e+2bZu3VorG+vLy8tDXl6e3gsmMkBSLRdRfXIJsOJQl2o6kxo0g6piyV/lpNn0kj6X7JPDavDqeR4uv/xyPPvss1i0aBG6d++e8f3u3bujqKgIr732Gg4//HAAwO7du7F48WJMnz7dxiUTGSOtlouoLrkGWKxLDZZFDZNBVbHkr3rSzCV90sFq8HrppZfiySefxHPPPYeCgoJ0jWthYSGaNWuGVCqFq666CnfccQd69uyJnj174o477kDz5s1x1lln2bx0IiP44CfJggRYSa9LDZJFDZtBVbHkr2PSzCV9Us1q8Dpr1iwAwJAhQzK+PmfOHEyYMAEAcN111+G7777DJZdckj6k4NVXX2WPV0oMPvjJpFyzg0EDrDjWparumuALm0FVteSvY9LMJX1SyXrZQENSqRQmT56MyZMn678gIqH44CcTgmQHgwZYYZajJdel6uia4Ae+YTOoKpf8OWkmyURs2CIiIruCZgeDBlgu1KXqyKSGyaKGzaCqXvLnpJmkYvBKRBQzQdsrhckOhgmwJNel6sqkhsmiRsmgsk6ekoDBKxFRjIRpUB8mOxg2wJJYl6ozkxomyI+aQeWSP8Udg1ciopgI26A+THYwSoAlqS5VdyY1SpAfJYPKJX+KMwavRETChDmXPkqD+rA1ljaWqFXXperOpEYJ8plBJaobg1ciIkHCnksfpUF91BpLUwGWjrpUE5nUKEE+M6hEtTF4JSISIsq59FEa1EetsYwaYNmsSzWVSWUWlUgdBq9ERAJEPZc+aoN6W7vUbdeljjq0o7FMKrOoRGoweCUiEiDqufQqGtSryg66VJfKTCqRexi8EhFFFGaDVU1Rz6VX1aA+anbQxbpUZlKJ3MLglYgogrAbrGpScS697Qb1LtelMpNK5A4Gr0REIUXZYFWTqnPpVQdhuWaV41CXykwqkRsYvBIRhRB1g1VNKs+lVxWEBckqsy6ViExpZPsCiIhcFCRYy5WfLSwqzAzsigrzA2VxVfCzyjX/G/2scskHpRlfD1uXmi2MTKEqUK5Zlxp0bPxAfnS/ThjYoy0DV6IYYOaViCiEqBusstGRLQy6oSxMVpl1qURkCoNXIoo9Fd0AalKxwSoblbWXYTaUhWnbZerkKdalEhGDVyKKNVXdAGpStcFKp7AbysJklVmXSkSmsOaViGIraN1mEH6wBqBW3WbQDVY6NLT0D1Qt/VdU1v6JsFll1qUSkQnMvBJRLKnuBlAXU31Vw5Q9RDmxK0pWmZlUItKNwSsRxVLU41ZzpTtYC1v2EGVDWdS2XaxLJSKdWDZARLGkqxtAXXQte0cpe4i6oUxS2y4iouqYeSWiWNLZDcCEqGUPKjaUsQSAiCRi5pWIYiloE3xpoh6CoGpDGTdTEZE0DF6JKJZsdwOoqPSwbN02PLd6E5at21bnrv76qCh74NI/EcURywaISCsdBwTkylQ3gJpU9JZVVfbApX8iipuU53nB0gGOKSsrQ2FhIXbs2IFWrVrZvhyiRNF1QEBQJgPobAcD+P9arhnPikoPg6YvbLBmdenEoQxEich5QeI1lg0QkRY6DwgIylTdZpSDAWqyXfZARCQVg1ciUk5lEOeSqJusamLNKhFRbax5JSLlTB0QII2O3rKsWSUiysTglYiUM3lAQBi6amB19ZbliVVERHsxeCUi5SQfEKBzE5mKgwGIiKh+rHklIuWkHhCgexMZN1kREenH4JWIlJMYxJnaRMZNVkREerFsgIi0sHVAQDYmN5FxkxURkT4MXolIG0lBnOlNZNxkRUSkB4NXItJKShAneRMZERHljjWvRJQIUjeRERFRMAxeiSgRJG4iIyKi4Bi8EpFxFZUelq3bhudWb8KydduMHRPLTgBERO5jzSsRGaXzkIBcSNpERkREwaU8zzOT8rCkrKwMhYWF2LFjB1q1amX7cohypusIU5v8QwJqPnT8/ypmP4mIkilIvMbMK5FAtrOTOjR0SEAKVYcEDOtd5HyQTkRE+rDmlUgY3UeY2hLkkAAiIqJsGLwSCWLqCFMbTB8SQERE8cTglUiQOGcneUgAERGpwOCVSJA4Zyd5SAAREanA4JVIkDhnJ3lIABERqcDglUiQuGcneUgAERFFxVZZRIL42cmL//guUkDGxq24ZCd5SAAREUXBQwqIBIpjn1ciIqJseEgBkeOYnSQiIqobg1cioRo3SmFgj7a2L4OIiEgUbtgiIiIiImcw80rkkIpKj6UERESUaAxeiRzBTVxEREQsGyByQskHpbj4j+/WOjp2y47vcfEf30XJB6WWroyIiMgsBq9EwlVUepjytw9RV087/2tT/vYhKipj3fWOiIgIAINXIvH+uf6rWhnX6jwApTu+xz/Xf2XuooiIiCxh8Eok3Nad2QPXMD9HRETkMgavRMK1L8hX+nNEREQuY/BKJNxR3duguDAf2RpipVDVdeCo7m1MXhYREZEVDF6JhGvcKIVbT+0NALUCWP/vt57am/1eiYgoERi8EjlgeN9izPrpj1BUmFkaUFSYj1k//RH7vBIRUWLwkAIiRwzvW4xhvYt4whYRESWa1czrkiVLcOqpp6Jjx45IpVL461//mvH9CRMmIJVKZfwZMGCAnYslEqBxoxQG9miL0f06YWCPtgxciYgocawGr9988w0OO+wwPPDAA1l/Zvjw4SgtLU3/eemllwxeIRERERFJYrVsYMSIERgxYkS9P5OXl4eioiJDV0REREREkonfsLVo0SK0b98evXr1woUXXoitW7fW+/Pl5eUoKyvL+ENERERE8SA6eB0xYgSeeOIJLFy4EPfeey9WrFiBoUOHory8POvvTJs2DYWFhek/Xbp0MXjFRERERKRTyvM8z/ZFAEAqlcKzzz6L0047LevPlJaWomvXrliwYAHGjBlT58+Ul5dnBLdlZWXo0qULduzYgVatWqm+bCIiIiKKqKysDIWFhTnFa061yiouLkbXrl3x8ccfZ/2ZvLw85OXlGbwqIiIiIjJFdNlATdu2bcPGjRtRXMyG7ERERERJZDXzumvXLnzyySfpv69fvx6rV69GmzZt0KZNG0yePBlnnHEGiouLsWHDBkyaNAnt2rXD6aefbvGqiYiIiMgWq8HrO++8g+OPPz7996uvvhoAMH78eMyaNQtr1qzBvHnzsH37dhQXF+P444/HU089hYKCAluXTEREREQWidmwpUuQAmAiIiIiMi9IvOZUzSsRERERJRuDVyIiIiJyBoNXIiIiInKGU31ew/BLenlMLBEREZFMfpyWy1as2AevO3fuBAAeE0tEREQk3M6dO1FYWFjvz8S+20BlZSU2b96MgoICpFIp25ejlX8U7saNG9lZQSGOq3ocU/U4pnpwXNXjmKoXhzH1PA87d+5Ex44d0ahR/VWtsc+8NmrUCJ07d7Z9GUa1atXK2ZtXMo6rehxT9TimenBc1eOYquf6mDaUcfVxwxYREREROYPBKxERERE5g8FrjOTl5eHWW29FXl6e7UuJFY6rehxT9TimenBc1eOYqpe0MY39hi0iIiIiig9mXomIiIjIGQxeiYiIiMgZDF6JiIiIyBkMXomIiIjIGQxeHXX77bfjmGOOQfPmzdG6des6f+azzz7DqaeeihYtWqBdu3a44oorsHv37oyfWbNmDQYPHoxmzZqhU6dOuO2223I6VzgJ3n33XQwbNgytW7dG27ZtcdFFF2HXrl0ZP5NKpWr9eeihhyxdsRtyGddc7l3a67//+78xevRotGvXDq1atcKxxx6LN954I+NneK8Gk8uY8j4NZtGiRXXeh6lUCitWrEj/HO/V3OU6pnG7Vxm8Omr37t0488wzcfHFF9f5/YqKCowcORLffPMNli5digULFuCZZ57BNddck/6ZsrIyDBs2DB07dsSKFStw//3345577sGMGTNM/WeItXnzZpx44ok48MAD8fbbb6OkpAT/+te/MGHChFo/O2fOHJSWlqb/jB8/3vwFOyKXcc3l3qVMI0eOxJ49e7Bw4UKsXLkS/fr1w6hRo7Bly5aMn+O9mruGxpT3aXDHHHNMxv1XWlqKCy64AN26dcORRx6Z8bO8V3OTy5jG8l71yGlz5szxCgsLa339pZde8ho1auRt2rQp/bX58+d7eXl53o4dOzzP87wHH3zQKyws9L7//vv0z0ybNs3r2LGjV1lZqf3aJfv973/vtW/f3quoqEh/bdWqVR4A7+OPP05/DYD37LPPWrhCN+Uyrrncu7TXl19+6QHwlixZkv5aWVmZB8B7/fXX01/jvZq7XMaU92l0u3fv9tq3b+/ddtttGV/nvRpeXWMax3uVmdeYWrZsGfr27YuOHTumv3byySejvLwcK1euTP/M4MGDM5oan3zyydi8eTM2bNhg+pJFKS8vR9OmTdGo0d63SLNmzQAAS5cuzfjZyy67DO3atUP//v3x0EMPobKy0ui1uiSXcc3l3qW92rZti0MOOQTz5s3DN998gz179uD3v/89OnTogCOOOCLjZ3mv5iaXMeV9Gt3zzz+P//znP3WuaPFeDaeuMY3jvcrgNaa2bNmCDh06ZHxt3333RdOmTdPLXnX9jP/3msuNSTN06FBs2bIFd999N3bv3o2vv/4akyZNAgCUlpamf+7Xv/41nn76abz++usYO3YsrrnmGtxxxx22Llu8XMY1l3uX9kqlUnjttdewatUqFBQUID8/H7/5zW9QUlKSUQ/PezV3uYwp79PoZs+ejZNPPhldunTJ+Drv1fDqGtM43qsMXgWZPHly1sJr/88777yT8+ulUqlaX/M8L+PrNX/G+9/NWnX9bhzkOsZ9+vTB3Llzce+996J58+YoKirCAQccgA4dOqBx48bp17vpppswcOBA9OvXD9dccw1uu+023H333Rb/C+1QPa653Ltxl+uYep6HSy65BO3bt8ebb76Jf/7znxg9ejRGjRqVMdHivap+THmfVgnz2fX555/jlVdewc9+9rNar8d7Vf2Yxu1e3cf2BdBel112GcaOHVvvz3Tr1i2n1yoqKsLbb7+d8bWvv/4aP/zwQ3oGVlRUVGvWtXXrVgCoNUuLiyBjfNZZZ+Gss87CF198gRYtWiCVSmHGjBno3r171t8dMGAAysrK8MUXX8R2DOuiclxzuXeTINcxXbhwIV544QV8/fXXaNWqFQDgwQcfxGuvvYa5c+fi+uuvr/N3k3ivqhxT3qd7hfnsmjNnDtq2bYv/+q//avD1ea/WLdcxjeO9yuBVkHbt2qFdu3ZKXmvgwIG4/fbbUVpaiuLiYgDAq6++iry8vHTN1sCBAzFp0iTs3r0bTZs2Tf9Mx44dcw6SXRNmjP0392OPPYb8/HwMGzYs68+uWrUK+fn5WduXxZXKcc3l3k2CXMf022+/BYCMOmL/7/XVCSbxXlU5prxP9wr6/vc8D3PmzMG5556LJk2aNPjzvFcbVt+YxvJetbZVjCL59NNPvVWrVnlTpkzxWrZs6a1atcpbtWqVt3PnTs/zPG/Pnj1e3759vRNOOMF79913vddff93r3Lmzd9lll6VfY/v27V6HDh28cePGeWvWrPH+8pe/eK1atfLuueceW/9Zotx///3eypUrvbVr13oPPPCA16xZM+++++5Lf//555/3Hn74YW/NmjXeJ5984j3yyCNeq1atvCuuuMLiVcvX0Ljmcu/SXl9++aXXtm1bb8yYMd7q1au9tWvXetdee63XpEkTb/Xq1Z7n8V4NKpcx5X0a3uuvv+4B8D788MNa3+O9Gk59YxrHe5XBq6PGjx/vAaj154033kj/zKeffuqNHDnSa9asmdemTRvvsssuy2iL5Xme9/7773s//vGPvby8PK+oqMibPHly4ttk+c455xyvTZs2XtOmTb1DDz3UmzdvXsb3X375Za9fv35ey5YtvebNm3t9+/b1Zs6c6f3www+WrtgNDY2r5+V279JeK1as8E466SSvTZs2XkFBgTdgwADvpZdeSn+f92pwDY2p5/E+DWvcuHHeMcccU+f3eK+GU9+Yel787tWU5/E4JSIiIiJyA7sNEBEREZEzGLwSERERkTMYvBIRERGRMxi8EhEREZEzGLwSERERkTMYvBIRERGRMxi8EhEREZEzGLwSERERkTMYvBIROWbDhg1IpVJYvXq1ltdPpVL461//quW1iYiiYvBKRBTQhAkTcNppp1n797t06YLS0lL07dsXALBo0SKkUils377d2jUREZmyj+0LICKiYBo3boyioiLbl0FEZAUzr0RECi1evBhHHXUU8vLyUFxcjOuvvx579uxJf3/IkCG44oorcN1116FNmzYoKirC5MmTM17j3//+NwYNGoT8/Hz07t0br7/+esZSfvWygQ0bNuD4448HAOy7775IpVKYMGECAKBbt26YOXNmxmv369cv49/7+OOPcdxxx6X/rddee63Wf9OmTZvwk5/8BPvuuy/atm2L0aNHY8OGDVGHiogoFAavRESKbNq0Caeccgr69++P9957D7NmzcLs2bMxderUjJ+bO3cuWrRogbfffht33XUXbrvttnTQWFlZidNOOw3NmzfH22+/jYcffhg33nhj1n+zS5cueOaZZwAAa9euRWlpKe67776crreyshJjxoxB48aNsXz5cjz00EOYOHFixs98++23OP7449GyZUssWbIES5cuRcuWLTF8+HDs3r07yPAQESnBsgEiIkUefPBBdOnSBQ888ABSqRQOPvhgbN68GRMnTsQtt9yCRo2q8gWHHnoobr31VgBAz5498cADD+Dvf/87hg0bhldffRXr1q3DokWL0qUBt99+O4YNG1bnv9m4cWO0adMGANC+fXu0bt065+t9/fXX8dFHH2HDhg3o3LkzAOCOO+7AiBEj0j+zYMECNGrUCI8++ihSqRQAYM6cOWjdujUWLVqEk046KdggERFFxOCViEiRjz76CAMHDkwHeQBw7LHHYteuXfj888+x//77A6gKXqsrLi7G1q1bAVRlT7t06ZJR03rUUUdpu979998/HbgCwMCBAzN+ZuXKlfjkk09QUFCQ8fXvv/8e69at03JdRET1YfBKRKSI53kZgav/NQAZX2/SpEnGz6RSKVRWVmZ9jbAaNWqU/vd9P/zwQ61rq3kt1VVWVuKII47AE088Uetn99tvPyXXSUQUBINXIiJFevfujWeeeSYjAH3rrbdQUFCATp065fQaBx98MD777DN88cUX6NChAwBgxYoV9f5O06ZNAQAVFRUZX99vv/1QWlqa/ntZWRnWr1+fcb2fffYZNm/ejI4dOwIAli1blvEaP/rRj/DUU0+hffv2aNWqVU7/DUREOnHDFhFRCDt27MDq1asz/lx00UXYuHEjLr/8cvz73//Gc889h1tvvRVXX311ut61IcOGDUOPHj0wfvx4vP/++/jHP/6R3rCVLSPbtWtXpFIpvPDCC/jyyy+xa9cuAMDQoUPxhz/8AW+++SY++OADjB8/Ho0bN07/3oknnoiDDjoI5557Lt577z28+eabtTaHnX322WjXrh1Gjx6NN998E+vXr8fixYtx5ZVX4vPPPw8zdEREkTB4JSIKYdGiRTj88MMz/tx666146aWX8M9//hOHHXYYfvGLX+BnP/sZbrrpppxft3HjxvjrX/+KXbt2oX///rjgggvSv5+fn1/n73Tq1AlTpkzB9ddfjw4dOuCyyy4DANxwww047rjjMGrUKJxyyik47bTT0KNHj/TvNWrUCM8++yzKy8tx1FFH4YILLsDtt9+e8drNmzfHkiVLsP/++2PMmDE45JBDcP755+O7775jJpaIrEh5dRU9ERGRGP/4xz8waNAgfPLJJxnBJxFREjF4JSIS5tlnn0XLli3Rs2dPfPLJJ7jyyiux7777YunSpbYvjYjIOm7YIiISZufOnbjuuuuwceNGtGvXDieeeCLuvfde25dFRCQCM69ERERE5Axu2CIiIiIiZzB4JSIiIiJnMHglIiIiImcweCUiIiIiZzB4JSIiIiJnMHglIiIiImcweCUiIiIiZzB4JSIiIiJn/H+6S6t3Hp1dHQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filepath = \"../data/Bingqing/PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\"\n",
    "dataset = hypercoast.read_pace(filepath)\n",
    "# Thanks to PACE OCI tutorials and notebooks: https://oceancolor.gsfc.nasa.gov/resources/docs/tutorials/notebooks/oci-file-structure/\n",
    "plot = hypercoast.view_pace_pixel_locations(filepath, step=20)\n",
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load field data and interactively display it alongside PACE data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Station</th>\n",
       "      <th>Station.1</th>\n",
       "      <th>Time</th>\n",
       "      <th>Date</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Depth (m)</th>\n",
       "      <th>Secchi (m)</th>\n",
       "      <th>Salinity</th>\n",
       "      <th>Water Temp</th>\n",
       "      <th>...</th>\n",
       "      <th>Absorption</th>\n",
       "      <th>CDOM</th>\n",
       "      <th>LISST</th>\n",
       "      <th>Nano</th>\n",
       "      <th>Surface \\npH</th>\n",
       "      <th>Sufface \\nO2</th>\n",
       "      <th>Bottom \\nO2</th>\n",
       "      <th>FL-ECO</th>\n",
       "      <th>FL-CDOM</th>\n",
       "      <th>Notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>River stations</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>R1</td>\n",
       "      <td>St1</td>\n",
       "      <td>09:39:00</td>\n",
       "      <td>2024-07-21</td>\n",
       "      <td>-89.45114</td>\n",
       "      <td>28.89887</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>28.71</td>\n",
       "      <td>30.74161</td>\n",
       "      <td>...</td>\n",
       "      <td>125ml</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>River Mouth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>R2</td>\n",
       "      <td>St2</td>\n",
       "      <td>09:47:00</td>\n",
       "      <td>2024-07-21</td>\n",
       "      <td>-89.45306</td>\n",
       "      <td>28.90001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>25.91325</td>\n",
       "      <td>30.91475</td>\n",
       "      <td>...</td>\n",
       "      <td>150ml</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>River plume, seaside</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R3</td>\n",
       "      <td>St3</td>\n",
       "      <td>09:59:00</td>\n",
       "      <td>2024-07-21</td>\n",
       "      <td>-89.43833</td>\n",
       "      <td>28.89487</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.75</td>\n",
       "      <td>24.44862</td>\n",
       "      <td>30.59565</td>\n",
       "      <td>...</td>\n",
       "      <td>100ml</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R4</td>\n",
       "      <td>St4</td>\n",
       "      <td>10:13:00</td>\n",
       "      <td>2024-07-21</td>\n",
       "      <td>-89.43162</td>\n",
       "      <td>28.90630</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5</td>\n",
       "      <td>8.34838</td>\n",
       "      <td>30.13989</td>\n",
       "      <td>...</td>\n",
       "      <td>100ml</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Station Station.1      Time       Date       Lon       Lat  \\\n",
       "0  River stations       NaN       NaN        NaT       NaN       NaN   \n",
       "1              R1       St1  09:39:00 2024-07-21 -89.45114  28.89887   \n",
       "2              R2       St2  09:47:00 2024-07-21 -89.45306  28.90001   \n",
       "3              R3       St3  09:59:00 2024-07-21 -89.43833  28.89487   \n",
       "4              R4       St4  10:13:00 2024-07-21 -89.43162  28.90630   \n",
       "\n",
       "  Depth (m) Secchi (m)  Salinity Water Temp  ... Absorption CDOM LISST Nano  \\\n",
       "0       NaN        NaN       NaN        NaN  ...        NaN  NaN   NaN  NaN   \n",
       "1       NaN          2     28.71   30.74161  ...      125ml  yes   yes   no   \n",
       "2       NaN          2  25.91325   30.91475  ...      150ml  yes   yes  yes   \n",
       "3       NaN       0.75  24.44862   30.59565  ...      100ml  yes   yes  yes   \n",
       "4       NaN        0.5   8.34838   30.13989  ...      100ml  yes   yes  yes   \n",
       "\n",
       "  Surface \\npH Sufface \\nO2  Bottom \\nO2 FL-ECO  FL-CDOM                 Notes  \n",
       "0          NaN          NaN          NaN    NaN      NaN                   NaN  \n",
       "1          NaN          NaN          NaN    NaN      NaN           River Mouth  \n",
       "2          NaN          NaN          NaN    NaN      NaN  River plume, seaside  \n",
       "3          NaN          NaN          NaN    NaN      NaN                   NaN  \n",
       "4          NaN          NaN          NaN    NaN      NaN                   NaN  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kml_path = \"../data/Bingqing/GOM_Hypoxia.kml\"\n",
    "filepath = \"../data/Bingqing/Hypoxia_Data_Sheet.xlsx\"\n",
    "df = pd.read_excel(filepath)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Station</th>\n",
       "      <th>Station.1</th>\n",
       "      <th>Time</th>\n",
       "      <th>Date</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Depth (m)</th>\n",
       "      <th>Secchi (m)</th>\n",
       "      <th>Salinity</th>\n",
       "      <th>Water Temp</th>\n",
       "      <th>...</th>\n",
       "      <th>Absorption</th>\n",
       "      <th>CDOM</th>\n",
       "      <th>LISST</th>\n",
       "      <th>Nano</th>\n",
       "      <th>Surface \\npH</th>\n",
       "      <th>Sufface \\nO2</th>\n",
       "      <th>Bottom \\nO2</th>\n",
       "      <th>FL-ECO</th>\n",
       "      <th>FL-CDOM</th>\n",
       "      <th>Notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>R1</td>\n",
       "      <td>St1</td>\n",
       "      <td>09:39:00</td>\n",
       "      <td>2024-07-21</td>\n",
       "      <td>-89.45114</td>\n",
       "      <td>28.89887</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>28.71</td>\n",
       "      <td>30.74161</td>\n",
       "      <td>...</td>\n",
       "      <td>125ml</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>River Mouth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>R2</td>\n",
       "      <td>St2</td>\n",
       "      <td>09:47:00</td>\n",
       "      <td>2024-07-21</td>\n",
       "      <td>-89.45306</td>\n",
       "      <td>28.90001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>25.91325</td>\n",
       "      <td>30.91475</td>\n",
       "      <td>...</td>\n",
       "      <td>150ml</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>River plume, seaside</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>R3</td>\n",
       "      <td>St3</td>\n",
       "      <td>09:59:00</td>\n",
       "      <td>2024-07-21</td>\n",
       "      <td>-89.43833</td>\n",
       "      <td>28.89487</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.75</td>\n",
       "      <td>24.44862</td>\n",
       "      <td>30.59565</td>\n",
       "      <td>...</td>\n",
       "      <td>100ml</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R4</td>\n",
       "      <td>St4</td>\n",
       "      <td>10:13:00</td>\n",
       "      <td>2024-07-21</td>\n",
       "      <td>-89.43162</td>\n",
       "      <td>28.90630</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5</td>\n",
       "      <td>8.34838</td>\n",
       "      <td>30.13989</td>\n",
       "      <td>...</td>\n",
       "      <td>100ml</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R5</td>\n",
       "      <td>St5</td>\n",
       "      <td>10:58:00</td>\n",
       "      <td>2024-07-21</td>\n",
       "      <td>-89.37324</td>\n",
       "      <td>28.98095</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.4625</td>\n",
       "      <td>30.3054</td>\n",
       "      <td>...</td>\n",
       "      <td>100ml</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Station Station.1      Time       Date       Lon       Lat Depth (m)  \\\n",
       "0      R1       St1  09:39:00 2024-07-21 -89.45114  28.89887       NaN   \n",
       "1      R2       St2  09:47:00 2024-07-21 -89.45306  28.90001       NaN   \n",
       "2      R3       St3  09:59:00 2024-07-21 -89.43833  28.89487       NaN   \n",
       "3      R4       St4  10:13:00 2024-07-21 -89.43162  28.90630       NaN   \n",
       "4      R5       St5  10:58:00 2024-07-21 -89.37324  28.98095       NaN   \n",
       "\n",
       "  Secchi (m)  Salinity Water Temp  ... Absorption CDOM LISST Nano  \\\n",
       "0          2     28.71   30.74161  ...      125ml  yes   yes   no   \n",
       "1          2  25.91325   30.91475  ...      150ml  yes   yes  yes   \n",
       "2       0.75  24.44862   30.59565  ...      100ml  yes   yes  yes   \n",
       "3        0.5   8.34838   30.13989  ...      100ml  yes   yes  yes   \n",
       "4        0.5    2.4625    30.3054  ...      100ml  yes   yes  yes   \n",
       "\n",
       "  Surface \\npH Sufface \\nO2  Bottom \\nO2 FL-ECO  FL-CDOM                 Notes  \n",
       "0          NaN          NaN          NaN    NaN      NaN           River Mouth  \n",
       "1          NaN          NaN          NaN    NaN      NaN  River plume, seaside  \n",
       "2          NaN          NaN          NaN    NaN      NaN                   NaN  \n",
       "3          NaN          NaN          NaN    NaN      NaN                   NaN  \n",
       "4          NaN          NaN          NaN    NaN      NaN                   NaN  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered = df.dropna(subset=['Lon', 'Lat']).reset_index(drop=True)\n",
    "df_filtered.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize PACE image and field data on an interactive map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d3e865d1b5fe4ba689dc7e56bdf31bfb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map(center=[28.89758, -91.46118], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title', …"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = hypercoast.Map()\n",
    "m.add_basemap(\"Hybrid\")\n",
    "wavelengths = [450, 550, 650]\n",
    "m.add_pace(\n",
    "    dataset, wavelengths, indexes=[3, 2, 1], vmin=0, vmax=0.02, layer_name=\"PACE\"\n",
    ")\n",
    "m.add(\"spectral\")\n",
    "style = {\"weight\": 2, \"color\": \"red\"}\n",
    "m.add_kml(kml_path, style=style, layer_name=\"Hypoxia Path\", info_mode=None)\n",
    "m.add_points_from_xy(df_filtered, x=\"Lon\", y=\"Lat\", max_cluster_radius=50, layer_name=\"Hypoxia Data Points\")\n",
    "m.set_center(-91.46118, 28.89758, zoom=8)\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checkout the demo: \n",
    "\n",
    "https://i.imgur.com/JdgotIT.gif"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://i.imgur.com/JdgotIT.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute band ratios."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "da = dataset[\"Rrs\"]\n",
    "data = (da.sel(wavelength=650) > da.sel(wavelength=620)) & (da.sel(wavelength=701) > da.sel(wavelength=681)) & (da.sel(wavelength=701) > da.sel(wavelength=450))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The spectra of cyanobacteria bloom:\n",
    "\n",
    "![](https://i.imgur.com/pQP50bz.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize the selected region based on band ratios."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pdJKZmcn1118/i6NbnFIMOqyKbs63q4gwhGMXv+Xl5U1qHeF0OtHr9WRmZs75/mZiQQbgkpRMUlNTcbvd8S5GQhBC0NLSwrp16+JdFElalEZHRxkcHCQvL++c1h8PrtNsQ2ys3kdjVyl3fWEImD7onolQSI9BF5zx8p/61Kd45JFH+PnPf84dd9xBMBhk69atHDlyBL/fT19fX9wCMGn+XHjhhfz5z3+eMO25555jw4YNcasYkAG4JMWZw+GgpaUl3sVICMPDw3i9XoqLi+NdFElalJqamgDYs2cPRqORmpoaYGa11qcqy2tndMxKU1fZnJYvGNZj0IdmvPzf/vY3ACoqKibNs9ls2Gy2OSvbYqDoFdQYNNlQxOy2OTo6SkNDQ/R5c3Mz+/btIyMjg5KSEj73uc/R2dnJL3/5SyCS8eT73/8+n/rUp/iXf/kXtm/fzsMPP8xvf/vbOT2O2ZABuCTFmawBP2m8h73Vao1zSSRpcRkPsEty21lWDM0tHbS2/h9761cx4J5dDXG63UV22gAHm2qAuQ3WgiEDel0IENNue3h4mJSUSFMVi8Uyp+VYrBSDiqLMfRtwZZYZsXft2sUVV1wRff6pT30KgDvuuIOf//zndHd309bWFp1fXl7O1q1b+eQnP8kPfvADCgoK+N73vse73vWuuTmAcyADcGlG3nzzTdra2ujp6cHj8fDYY4/FbF8Wi+WsyfGTPXX96eUfGRkhGAzi8/mSqlNOLIwH4LKtsCTNrZnWYHf25RMMGegbymTDsn3kZ/bOOgDPy3Di8VnoGcw5l6KeVTisQ1UFqqKhiTO3Re7t7eW6666jpaWF119/nQ0bNsx5WaT4ufzyy88aC/z85z+fNO2yyy5jz549MSzV7MgAXJqRPXv20NvbS2VlJQUFBfj9/pjuL9ZBdrx6PY/v+9T9G41GNE3D7XYv+gA8GIy07ZQBuCTN3Gybh5xNWNPTPZB34rEOTcy+ttNiGmPEa2Oua78BdLowQnDWcu3du5ebbrqJYDDItm3bqK2tnfNyLFaqTkFV5/51VbX4fSfHiwzApRlZtWoVTqeTq6++elIuzbk03kHmH//xHxdNEObz+fj2t7/Nq6++ytve9ra4p96LJxmAS9JkF9+4DaNBcPcdcO3NrxEIzk+woioa4hwCI7PRz6g3Nm2rfQETigJW0xhe/+Smav1dL7JlyzdYvnw5Tz31lOxPIiUsGYBLM7Jp06ZoDvBYBuCLkdls5m1vext//OMfaWxs5M4778Rut8e7WHEhA3BpMZrLGuy5pKoa4VnXgAvMBj++4Nzlbz6VayQNf8DIspIG9jWsRJxSPvfgIer2/Ce33HILDz/8sGz3HQOKQUGJQQ24ImvAJWlqdXV1hMNhCgoK4l2UBam2tpaMjAwefvhhOjs7qa6ujneR4mL8C9Pr9S765jjSwpGoAfZ0VEVD02YXgKemuNHpNIZHYzNWRFjTc7hlGWuWHKK24jAHGlcgUBFCo/nID0hxVNHq/hcZfEsJTwbg0oyMJ8b/wx/+wG233SZrwWPg2LFjmEwmSktL412UuElPTwfA5XJNGLhDkhJZsgbY01FVMaGGeSYyHS6CIT3DntgNhjfgzmR/4wrWVh0iM9VF/3AmfZ1/Y3ToKCsv/G5MsnRIEapetgGfKzIAl2akuLiYj370ozz66KO8/vrrvOMd74h3kRac1tZWli1btqhrblJTU1FVlcHBQSorK+NdHEmKWqhB9tmoikZ4ljXgjpQRhj12YtEB81T9w5n4A0aKsrtobnXSeuxHZORdQmrmmpjuV5LmigzApRnLzMzEZDLh8/niXZQFaXh4mPLy8ngXI65UVSUtLY3BwcF4F0VaZBZjgD0dRdFmXQNut47SPTD36QcnUzjSupTVSw6h9/4ZvcFG+fKPzsN+FzfZBnzuLMgA/OWXX44m358v42nzXnnllZimuFNVleXLl5OdnR2zfZzJ8ePH6e3t5frrr5/3fS8GVqsVr9cb72LEXUZGhgzApZiQQfbsqKqGNovAyGLyYjb6GfHOTyfy/uEsHn/iRbq6w6y+5Keo6oIMaRKKqlNQdTFoghKWAfiCcPDgwXnvwKUoCuXl5Rw4cCCmOaz9fj8vvfQSK1as4NJLLyUnZz5qGiK6urqwWCyLuo1yLKWnpzMwMBDvYsRdeno6LS0t8S6GlKRkkD1XBDpVzCoPeE3pcXwBI33Dsxu451z5vN0cPbSNpWu/KINvKeksyCv2rrvuinYanC/j+avvuuuumKZQC4VC7Nu3j1deeYWHHnpoykBc0zSGh4dxuVy4XC78fj/hcDj6D8But5Oamkp2dvaMOruFQiH27t1LTU1NzI5tsSsvL+fZZ59d9CNiZmRksGfPHoQQcR0wSUpcMsiOPUWJVCTNNAC3mj1kOoY40LgcTTvzCJXnK8XsITttAL0uiLNrDzp9Chm5F8Zsf9JEik5BiUENuBLjPgOJaEEG4AuZXq9nw4YNrF27dkIgnpWVRXl5OTabjTfffBOPxwNEauaNRiM6nS76TwjByMgImqYBsGHDBrZs2UJaWtoZ9/vaa68xOjrK5s2b5+MwF6Xq6mqeffZZdu/ezZYtW+JdnLjJyMggHA4zMjIy7zfSUuKQQXZ8GfUBAELhmQXTNkvkO2d0bO6bfyqKRl5GL7npfWSnDRIK6wgE9VhUQW7JW9HpJw/II0mJTgbgSUqn07F+/XrWrFnDsWPHaGpqoqGhgaGhIVavXs2KFSvIyMggNTUVnW7yB6gQgtHRUQ4fPsyLL77Irl27KC0tpaamhsrKSjIzM6O1j06nk5deeonLLrssLm3PFwuHw8HatWt5+eWXsVgsrF27dlHWAI//IjM4OCgD8EVABtqJyaAPAZDpGMSoDxLWVDShIk781YSCpqmRf0LFO2ZhzGeitvIgu+rWEg7rT9Sen99nmMngo7byCKkpboY9dg41L6NnMJdQKMDO5/+LnOLr5uBopZmKWRtwWQMuJRudTseKFStYsWIFAOFweMqA+3SKomC329m8eTPr1q3j2LFjHDhwgOeeew5N00hNTWXJkiVs2rSJoaEhgEU7OuN8uuqqqwiHw/z5z39m+/btVFRUUFZWRmVl5aIZon78l5jBwUHKysriWhZp7sznEOrS+fP6zPgCRgqzu1GV7hmvFwyrXL5me/S5JpgQtAuhomnKiecqIvp48jyjPki63UUgaOTNY+twn5JbXKczk1/2D3S3/IH80negqDpMllyZA1xKGjIAX2BmEnyfzmg0UltbS21tLYFAgNbWVhobGzl69Ci7d+9mxYoVrFq1iqeffhqLxcLy5ctjUHIJIsPS33TTTaxZs4b9+/dTX1/Pm2++SWpqKm9/+9upqKiIdxFjTq/Xk5qaKjOhJJkz1WQbDYK775jnwkjnLSd9ALMxwKsHL2DMb0FVNFRVQ1XEib8aiqKhquKUeRog0OsmzlOUk8tEHp+YPr69CfM0DGoIRY2MwtnYWUFnfx6h8OS+Vfnl/0hf5wvseem9AGQVXMmydV+a5zO1uChqjNIQisV3cy4DcGkCo9FIVVUVVVVVXHXVVezbt4/nnnuOVatWUVhYyN///neqq6tRVVnLEEulpaXRbDP9/f1s3bqVX/3qV3zoQx+isLAwzqWLvYyMDFwuV7yLIU1BNhlZHAqyenCNpDLmj7Sv1oQObYbtwedLKOAmq+AtdDY+CoAWDsS5RJI0czIAl85ovMOnEIKtW7dG8zPv3LmTTZs2xbt4i0ZWVha33XYb3/nOd3juuecoLS1FUZToP2DS4/FUmG+88QY6ne6sy57+eLrn87GeyWSSKRnjLFaBtlEfIMXiwTWSRqxHS5TOjdnoI8Pu4kjLsngX5Yz6u16ibs99oKhk5l9OUeUtpDjk6LmxpuhUFN3cV8ApxC59c6KSAbg0rY0bN5Kbm8trr72GyWTimWeeIScnZ9GP2jifFEXh4osv5q9//StOpzOa6lIIEQ22T32sKApLly7l9ddfJxwOn3G5Ux8nGtn+O/biUZud6RhkZcUxXth9KWIR/uycDPIze9A0lV5XYna6Hx2qo+HAN8jMu5SqtZ9Hp1u8aVul5CUDcGlGSkpKKCkpQdM0fv3rX/Pkk0/yyU9+UjZFmUcbN26kt7eXPXv2UFRUxA033EB6evqUy47npf/kJz8547z0MwnSxx9P9/x819u6dau8tuZQQjUbkTF3ghMUZPbQ68omrCVeiDA8sI+ju76AxVZK1Zr/kMH3PJNZUOZO4r27pISmqioXX3wxv/rVr3C5XGRmzs+IZ1KkVvttb3sbVVVVPPPMM/zsZz/jfe9735y9Bqc2B4mncDhMb28vl112WbyLknQSKtCWklKabRir2ceR1sRrfjLY+zpHd36e1Kx1VK+/X+b/jgNFiVEnTC3+3z3zTQbg0qyNj7rZ29srA/B5pigK1dXVFBUV8dOf/pSXXnqJd73rXfEu1pzq7u4mGAxGO6FKkyVSoL2suB5fwERrb0m8iyLNgYKsHrx+84k2+onF426M/B1u4PieB0hJW0pW/hWkOCZnh7r4xm28+md5Ey8lLhmAS7Nms9lITU2lvr5epiSME5vNxubNm3n22WexWCxceeWVC2b4+paWFoxGI/n5+fEuStwlUqB9Jo6UEfS6ULyLIc0BnRoiN91Ja08JidhWqLjqNuzpKxke2INnuJ6e1j/RUf8r8sveRcXKj8e7eIuCoiMmTVCUxOyKFFMyAJfOyaZNm3jhhRe47LLLzjqEvRQ7mzdvRlEUXnjhBQ4ePMhVV13F+vXr412s89bS0kJJSck55bRPVskQaJ9Jgvbhlc5BTnofep1G10BevItyRmlZa0nLWguApoXoavodrcd+QnrOJtJzLohz6SRp5mQALp2T9evX89prr/HEE0/w3ve+F5PJFO8iLUqbNm1i+fLl/P3vf+fpp5/G7XZzxRVXxLtY5ywcDtPe3s4ll1wS76LERDIH2tLCV5jVw9CIA18gOT7PVVVPYeWtDPXtpvHgt1h72SOT2oXv3r2be++9F6/Xy0c+8hH+6Z/+KU6lXRgUnYISkxrwxPvFJdZkAC6dE6PRyK233sovf/lLfvvb33L77bfLrBVxYrfbuemmm7BYLGzfvj2pOy92d3cTCASSPgWhDLSnZzH6zpiG8Nwq1c/vC/zcavLPbZ9n3ZUATahTjvwYa2aDD50uxFXrt6FpKmGhomkn/p14HD7lsSNlhKGRVAQKQigIFBBEn5/6OPKXU5ZTUBSN0TErQuhQFBHJBa0IFAWCIT19Q9OnQVQUhcpVn2LPS++lo/ExSpd9YML8N954g2effRaAV155hR07dnDVVVfF5PxJ0mzIAFw6ZwUFBdxyyy384he/4PXXX+fiiy+Od5EWtczMTILBYEJkMjlXra2tGAyGpGn/HatAW1XCaEIlEdvhnq+wFrlR37LqzTiXJLG1Ows43lGJps1fU6whTyoO6wiNXUUnh5c/McS8qmroTpmWk9aPqgqyUgcZ8qSiIFAUAUQC6PGAWlHGxyYQE6YpigYomI1nHr3y5f0X4g9OXxsf8EcG7RrseZXCin9Cb7BH533kIx+hv7+f559/nldffZWHH36Yq666inA4POMUrdJJiqqixKCyLRbbTHQyAJfOS2lpKTU1NRw7dkwG4HHW09NDRkZGUgfgLS0tFBcXJ1z772tvfo1AcH7Oa6ZjgOqSetqcRbQ7i+Zln/Opz5XFzmNrooHZdM65jvwcXq6zrzL7KvJzfSummD1UFrRg1Ac43FI9b/m4DfoQHl8KHX2FM1haYDIE8AeNnMvJ1qkhrlz3Koebl9LryplQa25PGWVTzR6yUgfo7C+Ydlspjkpyim+gr/N5jrz5OWq3fP/kfnQ6vvSlL3Hvvfdy6aWXsnfvXgB27NjB5ZdfPutyS9JckQG4dN4qKip4+umnaWlpSfqmA8kqEAhQV1fH0qVL412Uc6ZpGm1tbXG/kTu1VttoENx9x/zs12Tws6y4gdyMPgbcaQwMZ8zPjueZQGVoNC3exUho/cOZ+AJmVlUcISvtNZyubBq7qmK+X6M+gNtrn35BAJQZ1U5PRxO6STcYXp8FgIqC1hkF4HqDjarVnyE1o5b6/V8nGBjCYEybsIxOp+Pxxx9n1apVALz++usyAD8HihqjPOAx2GaikwG4dN7WrFnD4cOH+eMf/8jdd98d7+IsOj6fjyeffBKv18tFF10U7+LMiBACp9NJOByOTnM6nQQCAWw2G11dXXO2rzP9IvCBe3afVqjIH7vl5CSDPjLRZh4hqFfw+KxoYm5r5xU0inM7qSxoIaypHGyqoWcwh4XY/ESauV5XDsMHHeSm97G0uBFNMwGxHXfBoA8SCBpjuo9xJ9v/T/5lYXSknzd3NXLBhkrMxjF8AcukZaaSmrUOgJYjP6Rk2QdPbm90lJdffpmf/exnjI2NAZG24dLsxWwkTNkJU5JmT6fTsX79en7/+9/j8XhISUmJd5EWDSEETz75JO3t7dx8881kZCRHrWl9fT2//e1vJ023Wq386U9/mpcybJ5BCvtIx+JaNtbsQ9M0nEOZdDgLUFVxso2sMt5GVkSfK4qGbvyxKtApGsopbWnVE/MVRWDUBzEaArT3FdDYWR6XzndSYvIFzLT2FpNi9lCc20lsA3CB0RAkEJqf60+cuMGc6v44GBhm16FnWbrkVjbX7OBHP/oBAS2fVRf976RltbCfvq6/Y0kpwpGxivLld9F2/Oc4O56hvLyMnJwc9uzZQygUoqamhm9961tApMO3JMWTDMClOTGehjAYDMa5JItLW1sb9fX1vPvd76aqKvY/Uc8Vv98PwAc+8IE56wglhOADd++efsFZVLQY9ILaWo2dx9ZQmttETtoAOWkDk5YLa8qETBFCRLJFCKGgRR9H5gWDejRxcvmwpuJ0ZXPXk//O16778SyOWFos/EET/kBsa6Z1ahidqhGcrwD8LDXgWmgMp9PJQw89xOWXX87o6Ch68+TBnlzON2g69P/webswGNO54JqnKKj4J3KKrmOg91XKsnfz6quvUlBQQFFREaqq8t3vfpdvfOMbMmvXOZJNUOaODMClOTH+s57FMrOfCqXz53a7efbZZ0lLS6O6ujrexTkneXl55xyAT52BZKbtV2fGaBDAMKNjdnSqwOnK5FhbVTTQ1oR6IpA4/y8PGXxLZ+IPGjHoY1u5YTREtj9fTVDGqVN0xj2+76s40muxZ9Ty7LO/BqB2/cmRLv1jTpqP/ICB7m2kZq4jM/8yOht/ixb2EwwM0dn0OD0tf6BBRIL2oaEhsrOzWbp0abTd9ze+8Y3YH5wknYUMwKU54XA4AHjmmWe44YYbZHqnGPJ4POzatYs333wTvV7Pe97znqTLfCJmkXQ5cXJqC0JhPf6gOd4FkRaZvqEsakqbYroPoz6SDnC+mqBwIi/46cJhPwFfHwFfH6Pu+uh0z0gT/V0v4h/rxdX3Jjq9laVrv0hWwZV0Nf0fAHu3fQCftxNVZ6a46nYe/dkHqKqqIjs7O/oZGQwG2bp1KxdeeOG8HOVCoygxSkOoLL5fJGQALs2J4uJiVq1axb59+zCbzVx77bXxLtKCEQ6HGRgYoLm5maamJhobG1FVldraWq644oqkbHM/HoCffuOQOMH2ZIrCGQeOkaRY8gdNNPeUsgZYWtRAmzMXt8fOXHbUNerHa8DnKwCf+iY86B+MPtbCY9HHjQe+idGUhdVeRn7ZOylacit6gw0AW1oN5pRC7Gk1lFb/C2nZ69Eb7GzZsiW2hyBJ50EG4NKcUFWVd77znQQCAXbs2MHSpUspLy+Pd7GSkhCCI0eOsGfPHgYHBxkeHkYIgaqqlJSUcOWVV7J27doF0dznine+ghDJUfOhIKIdxyRpvrX1FgFuctL7yM/sJBDS43Rlc7y9ck7yhBtONEGZrzbgJoP/xIiXJ/cnhKCn5Y8ApDiW4nEfx2IrJbf4bag6PbnFb0XVTWwiE/AP0tX8OD5PJ35vL4pqJCN36tptIQQNDQ2xO6hFQLYBnzsyAJfm1BVXXEFdXR2h0OQOM9L0QqEQP//5z+ns7KSsrIwVK1aQlpZGRkYGRUVFGI3z2z5zrpxes52f2cPKJLs/UxRxrmOkS9IciAQorx3chNk4SnZaPyW5nZiNPo60VJ93Tm6jPkAgpEcQmxtioYXwjznx+/oJBYZYvVwlHIaOjjb6nC/iGT7O6FAdY552ymr+lcLKmxEiMlrmVE3stHCA4/u+xkD3iwAYTJkE/QM427eSlr2B7IIrJ63z5z//mfe85z1TZmCSpPkmA3BpTpnNZlRV5S9/+Qt33XWXbAs+S8eOHaOzs5P3vve9VFZWxrs452RmzUjEhD9JQUHWgEtxJ4hkzsnLcKJTNezWUS5etYPugVxaeovx+s6tSZrRECQ4g+Yn/jEn7sGDpKRWoSp6NC2IpgUQWgBNCxL0DzHY+xoedxNWezmqqsPjbsI72oLQTnYkDbgqqDvoYN++faiqiZTUJaRmrads+V1k5G4Gzt4uWIhwNPgeXzY9ZzNWewWpGauByGfR079azT//8z/zxhtvMDAwEP3lcDb9UKSTYpYHXFt8n60yAJfmVGpqKsuXL+fQoUMEAgEZgM9Se3s7aWlpSRF8n0977ZMJyJLnQ1dByDbgUtzp1BBrlhwiGDKw5/hqAiEjhdldlOZ0UJjdg9OVSUtPCcOe1Enr9ndvo/34L1ix6X8wmifmFPf6LKTkjpGV2k//cNakdUMhL/1df6flyEOEQ56zltFiK8Oevpyx0TaEFiIltYqc4uuw2EowmbMxmNLQwn5CwVHWXqbHYitCUWY3wJVOb2HzdX/FO9KMJaUYvXHqDEiHDx9m69atk6Y7nU6KiopmtU9JNkGZSzIAl+bceEaUwcHBpOwgGE95eXns3LmTo0ePUlNTE+/iRM1558gk/KxVFJFUFfbSwlSe14ZRH2RX3ZroCJGtPSW09RaRn9FLaV47F9TsxTXioLWnhL7hTMbfcKHgCN6RJna+8C42XfsX9IbI57PNMkq6fQiA3Iy+aADuH3Pi7HgWV+92RoaPgdDILryGkmUfwOeNjFarqgYU1YCqGlFVA6regsmchRAC70gzJmseer11ymMxWXLP61zo9Bbs6WcfUWvLli2MjY2xf/9+du7cicvlihxn7vntW5LOlwzApTl3+eWXs3//fg4fPkxxcXG8i5NUamtraWxs5PHHH+fDH/4weXl5816G+chEoiRrKCtrwKW4ibxninM7OdZWNml4diFUugby6RrIIzt1gLL8NtZUHWJ0zEprTzHdg7nkFl1H44FvAuAe3E9B8TpqSuvISnUx5jdztLWKjt5M+nteord9K0N9O1F1JtJzNlNZfD2pWWuxpERqjc3WqT+btLCfjobf0HrsJwCU1txJUeV7YnVSZsRsNrNp0yY2bdoUTUMonRtFjVEawkU4MJIMwKU5ZzAYKC8vp6urK95FSTo6nY53vOMd9Pb28vLLL/Pud787pvuLf9q/5AloIzXgyVNeaeHQ64JUlzQBuTT3FNPac7aKDYW+4Sz6hrNITRmmLK+NFeV1VBY209ZbRFPRZfR0bCPod5FuHyIr1UXPYDZv7DfR0/YkfR3PEwq6saevoLL238jKvyJaU342QggGe16h4cA3CQXdkZKoBgwGOy1HfogtvQad3kpH/S9xDx6kev39ZOZfOjcnSJKSkAzApZhwOBwyAD9HOp2O4uJiOjs753S78Q+2J0q2PlCRNuDxLoW02Ngso6xfug+9XgFyaekuY6Y3rsOeVPY3riLF7KE0t50lhc2U3nYxP//5cXR6K90DuWQ5uklL6eLIjm+iCSs5xdeTW3w9VntZdDtCCIIBFwFfP1rYj6YF0MIBQoFhAv4BAv5Bhvp2MTbaOmH/QgvScOB/piyboiZnRqfFTrYBnzsyAJdiwmw2R4enl2YnHA5TX19/XsPLX3vza9x9R+RvIJh4H2zKFMNPJzpFZkGR4sBhHcFoCLG3YQ3r1p7bNjy+FI60VtPYVY7m+lqkA+KKWkaG6vjVz/+HrEwbjqzLsDrKCfpdHN/3VfQGOyn2CjQtwFD/Hnye9im3rTfYMZgyJwXfVnsZJkseQb+L0eHjgCDFsYTy5XfhyFy9KEc+lKRTyQBcigmz2YzP50MIkXTDpMfb7t27GR0dZePGjTNe5/TabWMSJJ9JvhBcyDbg0rzrceWwpLCZgsxe4Pw6Dg4PD7L7pZcAGOh5leYj30ens9La0gU0oOs1o9PbSM2sJRwaY9C5HZ3OFBlhctkHMVnz0OksqDpjtHmJqovkH6/f93WcHc9E9+XzdOMdaZl1UxYpscka8LkjA3ApJsZHbpTB9+wMDQ3xwgsvsH79enJycqZcJtGakpyzJAtmZRYUKR6spsgviaqindd2hBDs3faB6POmQ98BFELaMADrrvgVlpTCWW0zFPLicR1mdLieYGAIRdEDgvSczTgyVpGecyFWe+l5lVuSFioZgEsxMTAwkLSjNsZLKBTij3/8IxaLhauvvhpYQMH2AqCAzAMuzat02xAbqvcx5jfR2F3G1fjPeVu9bX9GC59sFmixlZFX8layi64mFBydUfAdSS3YhMv5Bi7nG4y4DiFEGEXR4ciopazmI2QVXoXRlH7O5ZQSm6wBnzsyAJdiYmxsDEVR0DQNdRGmF5qtsbExPvfFH+FIGWFv/Sqe+Mcd8S5STCVjbXKk3fri+5KYnjwnsZLhcBEM6Xnt0CYMegXOIQAfG+2gte6nDHS/BICi6Fmy+rOkZ2/EYEoDwGBMm7ReKDjKmKcDoymD0aE6XM4duPreIODrR9WZSc1aR/mKT2BPX44lpQid3jJpG9LCEwnAY5GGcPF9jsgAXIqJzZs3c+jQIZ5++mlqa2uBk0P/CiGi/05/Hg6HAairq4sG7mdafrptxXvZUx9brVYuvfTS6K8C4zXbVpOXouwu8jJ7STELdtetnnIEu4UpyT5wFZkF5VRGfYDC7C6sJi/DHtm2NxZ0ahh/0IgQKufSa2Kofx9H3vz0iSHgFUBQvPR2Gg/8D5oWoGTZB8jKv5Jw2IvP04nH3YhnpAmvuxH/WO+EbVlSisnMv5yME81Lxtt+S5J0bmQALsVEQUEB1157La+++ip79+6d8XqqqlJbW8tTTz2Fpp17m0dFUaL/4vl8/HFbexf/94dGjrVXoWmRIZetZg8XVO9BCJXuwRzaeosmDa6xoCVZMCuboEy0puogdssowbAekzEY7+IsSMGwnhSzl+KcDpxD+bNaV9OC1O3+0ongG8bfcG11P4su01b3swnPjaYsrI4KsgquxGovx2wtIBhwkWKvxJxScN7HIyU/RVVQdTFoghJefJ+tMgCXYuaCCy6gqamJzs5Obr/9dvR6/VmDVEVRCIVCbNu2jbvvvhuDwTBh3kyD3Xibqt12YVYKy8uOk2YfZn/DSjy+FCrzWwmFDWw/vIGwtrjeisk4EqYciGcinRrG47MSCusRmmxmFgvtzkKspjGWFjdgMQeBmbetHurbGR0QB0WldNkHUXVmFEVHZ+NjpGatIyPnQvQGG6regtmaN2VTFEmSYmNxfetL80pRFK655hp+8IMf0N7ezvr166ddJxiM1NZYLJZoAJ7oZtJRsrO/gNGxFFZVHGXz8l3sPr6aYFhPKKxbdME3AEmYUzsyEE9ylTmWGrvKWF15hOFRe7yLsmCFwgYOt9QQCBkoye1iNgG4JSUyWqbRnIM9vQZNC+IePETJsvez4S2PxajEiefiG7fx6p8vi3cxFgzZCXPuLMJvfmk+ZWZmUlhYSEtLy4wC8ER3PllJhj2pvH5oIxfU7GH90n34AmZC4cX5FkzKj9qkLHTsuD2RwFtVzy89njS9lp4SinKcABTnttPYUTLtOhZbMeUrPs6I6zA+TxfD/XsjNeJCY/mm/451kSVJmsbi/PaX5lVZWRlvvPEG3d3d5OfPrh1jPMUiBaAmdLx5dB2blu9C01SOtVXN+T6SQ/J1aJQ14BMZDZFfq5LsZUxKwZCRN4+uZ8O6MZYUtBAIqLQ7i6Zdr6D8XVD+rujzjoZHaa//lRwgTTpniqrGKAvK4mvGdl5H/LWvfQ1FUbjnnnui04QQ3HfffRQUFGCxWLj88ss5fPjwhPXq6urYsmULRUVF3H///RPmlZWVoSgKO3ZMTMN2zz33cPnll59PcaU4ueSSS8jMzOTHP/4xv/71r/F6vfEu0pQuvnHbhH+xoteHSDGP0dFXuIgyniQ/RZEB+KnyM3rxB4yEw7p4F2VRCIYiGZTaeouoLmmgMKtr1tsQQkNR5euVbGSstTCdcwC+c+dOfvzjH0dTzI37xje+wbe//W2+//3vs3PnTvLy8rj66qsZGRmJLnPXXXdx22238cc//pE///nPvPbaaxO2YTab+exnP3uuRZMSjNFo5P3vfz9vf/vb6e7u5rHHHoum6YuX04Pt+RzwJietHyEUel3Z87bPhKNE/0sSAllheJKiaORl9NI9mEtyvY7Jr7GrjDZnATWlx8nL6JnVui7nDhxpK2TtdxJJtFhrvA14LP4tNucUgI+OjvLP//zP/OQnPyE9/WSnECEE3/3ud7n33nt55zvfycqVK/nFL36B1+vl0UcfjS43NDTE2rVrqa2tpaCggOHh4Qnb/8hHPsKOHTvYunXrOR6WlGiMRiNr167lHe94B+3t7XR2ds7r/uMVbE9FrwsRCusIhZOjk2ksKCTfQDwg0xCOy8voxWgI0dWfF++iLEIKLd2l+AImVpYfw2r2zGitMU8nI65D5BRfH+PySXMlEWMtGYDPnXMKwO+66y7e+ta3ctVVV02Y3tzcTE9PD9dcc010mslk4rLLLuP111+PTrv//vu5+uqrsVqtqKrKtddeO2E7ZWVl3HnnnXzuc587r1zQUuKpqKjAbDazZ8+emO0jnrXbM7b4PmsmS6JgNjIKZvJlbokFBY2K/DZ6XVl4fHIAnvlWU1LHxat2YNAHaXcW4vObZ7Te8MBeQCUte0NsCyjNGRlrLWyz7oT52GOPsWfPHnbu3DlpXk9P5Oew3NzcCdNzc3NpbW2NPr/hhhvo6+vD7XaTnT31z/Bf+MIXeOSRR/jNb37DbbfdNqsy7tq1C5vNNqt1ztf4xbt792459PpZCCGorKxk//792O12LrroognnazwN4fjfmbj25ok/qxkTvGI5GLJiNmrYrT78wdiMJmc0iAl/E41eHxl0KVHLN+7kedRQVRWDLnHP6XzJTe/DZvVztK0Go0Gg0ynodcoZz0uiX4vJQFGCVBW1AVlkprpp7q2gZyCPUFiPXg/TdYUVQjA68AoZ2VVYrSnTLr/QnPp9ci7fMbEqy9kkaqwlO2HOnVkF4O3t7dx9990899xzmM1nvus+vX3ZVD2uTSbTGS8IgOzsbP793/+dL33pS9x8882zKSa9vb0T2kHNp97e3ukXWuQMBgO1tbW43W6eeeaZKZd5/vnnZ7y9u++Yq5LNFwNQS22tD/DFdE//eqs7pts/d6lAKhvWD0+7ZCK48xYPkdcMIDnKHDsmIudCI3IuygC4ZprzkrjXYrLIAmD9uqVEErrOrOlJ1Ps+d+LB4rt+p2piMZvvmLk0kyQEyRJrSednVgH47t27cTqdE/I5h8NhXn75Zb7//e9TV1cHRO7OTk0353Q6J92pzcSnPvUpHnzwQR588MFZrXfNNdfgcDhmvb/zEQwGef7557n66quTZgCZeOvu7uZvf/sbHR0dVFRUsHbtWgYHBxkYGODSSy/FZrNNqt1eKDYv38XQiINj7Utjsn2jQfCvt7p56FEHgWDiNZsozWulKLOH1w5vindRzmr8PP7oMSsXLt/B4eZqnEOLt/Nsdmo/KyuOsqtuNSPeyGfs2qr9+AJmjrYum3KdRL8WE1EoOMqI6ygjroN0Nf8egCWr7uCBz1836/MYDLjZ/ff3AJCWfQHV6++LRZET2rO/2xJ9HO/vard7+hvRRI615EA8c2dWAfhb3vIWDh48OGHa+9//fqqrq/nsZz9LRUUFeXl5PP/886xduxaAQCDAtm3b+O//nn3if5vNxhe/+EXuu+8+brzxxhmvZzAY4hYEx3PfyaakpIT3ve99HDt2jOeff57HH38c0LFmzSq+/j+P8NrBdSzUxtKdfZkU53QRCi1BE7FLCxYIKgkZ9IRDENa0hCzbVEKhSDOzQIikKXMs5GZ0MjBsZ2D4ZPrMcFgQCotpz0uiXovxlJoyTGluO2ajH1XV8PiMvPzi0xw5OLF2Nj1nM1mFNwPDsz6P/d17GBsbA6AwffOifA2m+k6O13f1TPaZLLGWdH5mFYDb7XZWrlw5YVpKSgqZmZnR6ffccw9f/epXqaqqoqqqiq9+9atYrVZuvfXWcyrghz/8Yb7zne/w29/+lk2bEru2TJqdUztHKqzCaAiiqDrWrBnFZvGQ4XAx6M6IYwljp2sgj7K8dkrz2mnuLot3ceIiGVuhLuYsKBbTGJmOIQ41V0+YnoyvYyLIdAywZskhPD4rbo+dkCZQA7sQoQ4AUrPWk5V/Bba0ZaQ4lpzzflx9J9sQp2Ul/2jEi0Eix1qyDfjcmfORMD/zmc8wNjbGRz/6UVwuF5s2beK5557Dbref0/YMBgMPPPDAOV9UUuI4WzYSgYo/aCInbSg6TVUWbq/sMb+V1t4iKgpaCASNdPYXxLtI80tJrrBNiZZ38QbguelOQmHd5Pz1QiEnrZ/UlW9MuZ5OpwJVbFq+i3B46ve0gkYkOeVcnl+BqkRGXBXnN+bcbHc7o4V0ujAD7nT2N65E0xQaDvwPzvatGIzpXHj9s6i60ztoz/49Ew55cfVGsmKkZm3EZMmZ9TakxCRjreR33gH4Sy+9NOG5oijcd9993Hfffee0vZaWlknTbrnlFm655ZZz2p4UP7NN/2c1eVlVeRhYwe66WvqHF/YokQ2dFehUjeVlx7GYxmjorGBxBXjJc6zKieAnzuNHxZHAbh3FFzChaRObTDX3lJDpcJ1xTb0u8jr3D2USCk99AnPSnQih0DeUNaOymAwBAkHjaQG7wGz0YdQHsZh8mI3+6OBJo2NWhkYdhMLjX3nxvfZMBj/5mU721ZchhErToe/ibI90FKy9+MEpgu9z03ToewQDQwAUlL9jTraZjC6+cRuv/vmyeBfjvCRMrKUoxGRUskU4ONSc14BLi9f55tteUX6MQDAy3LLbm0q8vyRjT6GufQljfjNLixtJMY9xuGXZohigRyG5gtnx74bF2QRFUFHQQl5GH0dbqybNHXRnnLWpWCT94DCNXeVnbH9st44QDBmo76ictjRLCpvIz3TidGVxoGk5QkRqt2tK68jL6CccVhnyOGjpKWHY48BhHaGqqImi7EjqtkDQwOuHN0aHdp8PwcAQ7sFDeIbrKai8mVSbID/TiaoKhNDoafsTANlF12K25k+ztZnpaf0Tzo5IlqmU1KVk5F40J9uVFjdFiVEnTBmAS9LMzfUAN0ZDgIHhxZZhQqHNWYzXb2Fl+TE2L9/NgabluD3zm8Vn/iVR9A2cLO/i+pIwG8dYXnacTIeL+o5yOvoKY7IfVRFo4uzNRBRFo7KgmfL8dgBy0vu5fM2reH1WwppKut3N8fYKWnuLOfV1GvHa6R7MJdPuoiCrm5z0AZYWNXKkdVk0eI+lcGiMfS9/iICvHwBNC6BbEhkQxaAPoSgqVlsp3pFmUhzT34BMp+Xoj+hs/C2nnoPai75/3tuVJGluyQBcmrFYjyjpcqdRkN0NZMZ0P4mofziLHUfWs6riCBuX7aWhs4LW3iIWbMCnJNmoksriaoKiKBpF2V0sKWwiGDKw53gtAzHsEK0oYspfFxxWN0uLGwlrOvS6II6UkWiQbbN4yHQMkmL2oqoax9uzaOstwmEdIazpJozSqWk6+oaz6BvOpDy/jSWFzQx7HDG7oYBIKkFVNeIfcxLw9ZNbciO9bX+ms/G3qGoILlyBXhcCQKezADDct5uC8nehKOeeGck/1gdA+fKPkpF3KYqioOrmr7ZfWthkJ8y5IwNwaUrxGL79WFtVtL1oSW4bDR2l816GePIFLOyqW8uSwmaWFjeSbndxqLlmQTZJUSCpKsHHQ8OkummYIVUNk5U6wJjPwqgvhezUASoLm0kxe+noK6C+o4KwFtuvClXRTgvABcU5nSwtasTjs6IJBZ2qsevYWoY9kb4ho2M2RscmjnhsNvrYtHwPAC09xTR2lZ3WZl2hsy+P8vwWSnI66OgrYK5vcgN+F93NT+Bsf4aAv5/UrHUA+Dyd0WXyy25FE/ujAXha9kZGho7g6nuDY7vvY+naL6A7h3bgQghGXAfJKb6egop/mpsDkiQpJmQALgHxCbhPpwkdx9qXci3DVBa0MjyaMsNOWQuHECr1HZW4RtJYUX6UtVUH2X189aSOb8nMZPBRkNmDP5hEtXJJlrVlpvS6EOuX7cNhHQUiNfyKAoPuNA421TA6dm4ZFWZLUQSapkbLtLz0GLkZ/bT2FlLfUTnjpiKZjkEAmruLKcntIDc90m59wH3yV7VAyMSxtiqWFjWSndZH39DcZgbpbn6CjoZfR58P90duCIYH9kSndTU/QeiC6mgAXlx1G8MD+3AP7mew5xUOb/8kFavuwZY6s4G6wmE/Pa1/or/r7/jHeskuvHoOj0iSTpID8cwdGYAvUokQcJ/NgDuNyoJm+oYyWbDNMM6ifziTvcdr2bBsH6vKj7C/cSUL5TxUFTUhgEPNNfEuyoxFz/wC6YRp1AcoyOomP7MXkyHAG0fWYTZFMoi4RlJPNN+Yv2ONpHkUVBU1UpjVBcD+hhWzHHVUkJ/Zy9Cog4bOSjr786kpPc66pQfpHsjhePsSAic6XurUMDo1zJolRxgda8Hpyqa9r4BA8NxqnQe6t2FOKcQ/1hsNvnX6FNJzNtHf9Xdyim8gLXsDCgqqzkRq1jpC4X3RAFxR9VRveICDr3+MsdE2fN4u9r/yYVIz11FZ+29YUs7eVObYri8y1L8LR/pKKmv/nbQTte6SJCUuGYAvEokecJ+urbeY1ZX7qSpqPJEZYWEEPrPh9jo40LSC1UsOUl1Sz7G2KhbCeTAZ/QyNpk5oo5ssFkI9uKqG2VC9F7PBz7DHwcGm5YyO2XB749fxV6+GKMzqQQDtziLanQX4g+ZZbcNuHSXdPsze+shAJWN+K3uOryY/s5elRQ1ctPJN6jsq6BnMoTy/jZ7BHHpdueSmOynO6aAop5OdR9fh9Vtntd9gwEXdnvsmTLvwhufxebvZ/8qHySv9BypX3TNpvVBYj0Efij43GB0sW/cl9r38IarWfJ5wyEvrsR9z4NU7Kai4GYvVClyJEIJTPwdGh+oY6nuT0uoPU7RE5nCWYku2AZ87MgBfoJIt4D7d0Ggax9qWUF3SQE7aAK29RXQN5C2ophgz0T+cydHWZawoq2No1EHPYF68i3ReHFY3GfZhmruL412U2TnRBEWnhjDoggSTuF1+fkYvVtMY2w9vTJiboIPN1RRl91DfUYn/HGqhITJQUDCkP62zqEL3QB79wxksLWpkedlxKgta0OtCNHZV4AuY6R/OJD+zh5XlxzAYguCf3X6Npgzyy99Fd/MToKhUrrwHVTXQ3fwkOr2V0poPT7leMKyP1oCPs6RE3hdB/yA5xdeRlrWe+v1fp6PhN1jMRuBKjrz5HxRWfRSrvYxgwM3RXV/AllZDXunbZ1dwSZLiSgbgC0SyB9xTaXcWMexxUJrbTnVJPYoiaHcWxbtY866rP/9ELV1X0gfg4zV+yda2X69Eyr1u6SGEgM6+fPwhIylmL8GQgf7hTPqHM0j0XyhSU4ZZUthMrys7YYJvgKHRDIZGzz3LSnZaH2V57bT0lEzZXjwYMnK4pYbugTyWldTT0VeAL3Cyhn28/XlxdidGXZABdzqamPnNfsmyDxIOjeFs34p/rBcAvdGOFvbxxjM3UL3hATLzLpmwTmiKAFzVmdAbU/H7nNFt1Gz8SuSxLgB48bjr6Wp+gspVn6LxwDfRwj6q1/8nesPEDqmSFAuKGpv22sriqwCXAXiyWogB91TcHgcHm1bgLzpOum1oUQbgAF39edRWHsVq8s76J/JE4hpJZcxvorKwmT3HV5PoAeu4giwnbk8KPYO56HRhCjJ70alhRn1WbBYPxTld9LqyOHjKwDCJwqgPEAzrEUKlqqgJVQ1T1zZ5QJ1klWYbYlXFEXpd2TR0lp912cGRdLYfvmDS9F5XNsc7KijI7CE/00kwpKO+I9KOHBQM+gDhsO6MQfnh7Z9idPgYeoMD/5iToH8IhCAc8gIgtPCkdUIhPSbL5Op2e1oNve1/paD8n9DpLdHpqhr51UULB7CnVdPb9jQDPS9Tvf5+OcS8NG9kJ8y5IwPwJLFYAu4z0akCi8kX72LETd9QFsGQnvL8Vg63RLIn6FTtRCaR5Png0oSOo61LWbf0IFmpA/QPJ0dNuILAZAxEB3lp6jo10BPkpPexqvwoNaXHOdJSHa9inkZQVdREaW47gaCRvQ2rCGsqobCBQCh5m9CcJMjLcFJTepyh0dQTnXrP9b2g0NpTQmtPCSlmDyW5HSwvO06GfYjBEQdLi5sYHbOdMSNRatY6RoePYTBlUFr9Yfa+/AGC/khGFqu9jMy8iyetE6kBnxyYl6/4GPu2fYDWup9SseLjU5RVY6DnFYb6dpFbciOZ+Zee4zFLkhRPMgBPUIs94D7dQsy/PBua0FHfUUFN6XFsVg82swdVFYTDKiNjNlwjqQwMZ+IaTSXRA/IBdyZuj42i7O6kCcBNRj8mQxC9LjRFXnYFpyuHI2qYleV19AzmnHVo9vlSWdhMaW47TV1lZKf3s3n5bgA0TUFBJPF7KpIjvDCrG7vVQ/dADkdbl87ZLw8eXwpHW5fhGkljWXE9qbYhhFCwWUZYVX6EA40rEEzcV2Hlu+lsfJS8krcRDo1Gg2+Awor3oKiTv2qDYT2G05qgAFhSiiip/hdajvwAe1oN2YVXTVpmdOgYJcveT0G5zPUtzTNVjfyLxXYXGRmAJwgZcEvT6ewvIBjWU5TdRX1nBV6fBat5jFTrCPmZvZTnt9M/nM7BpuUk+lu715VNeX4bkbwiiR8IDoykk2YbZGlx4xlruLsH8ijM6qGm5DgHm5fj9sQvq0hW6gAV+W3Ud1TQ0lPCmN/MyopjtPYW0uEsnBRAJjoFDZ0ujELkxqI4p4u+oUyauktxumLT/KJnMJdeVzaZjn7Ks3cAGjnpgnT7MIMj6ROW9bibAHBkrsZgysSevhKjOZuhvjcZ6H2NnOLrJm1/qjbg4wrK/xGPu4H6/f+NyZKLI2NVdF7Nhv/CaFt5TgP1LGYX37iNV/98WbyLIUlRif0tvYDJgPscJH6cFnNOV87EgGN4/IGgKLuLmtJ6MuxDDHkSu2bZ67eg14XPUKOceFRFIxTWTTlc+kkKR1qWUVt5mE01e+gbymB0LAWv34rLncZYwHKWdeeOomhUlxynfziDlp5IVo3uwTz6hrMIhZPvIz/VNsyq8iNYTJH20mFN4WhrVUyHkR8nhEr/cA7urr1cfPHFdPTlMziSdsbldXoLBqMDoymDge4XATBbC6ZcNhTWo9NpKIo2qfZeURSWrPo3/N4eDu/4d3KKr2XJyg8AkeYugaD8MJTiQ1EUFCUGbcBjsM1El3yfxklKBtznJ1JPuhCyMMeKgs3iIRDSM+BOR5fg2RpTzF40TSGcAGklc9Kd5KT1o9eFGfObaXMWoiqRdt25GS6gnPK8NtqduTR2lZ11W16/lR1HNlCY1U1uRh95GU7MRj+KAkOjDho7y0+pPRUnbkD0nOnu0qj3k2ZzI1AY85tmNDJlaoobi8k/qU10MgbfOjXE6orDjAXMHO+oRAHcXjtj/vm5mQFwOd/gyJsvsmfPHlZe8gemeq1Gh46i6syYzNkE/IMM9LwcnZeVP3Wt6/jrYdCFogMEnUrVGanZ+FW6mh+ns/ExbLYM4B/m4IgkSUoEyfeJnCRkwC3NJ7NxjMKsbhq7yglrenS6xL1ZcVjdVOS30tmfH/eMITbLKLUVRxjx2vAHTeRn9lKS2wlAKKyLpsbbfvgCRsdm+pO/Qmd/AZ39kZpPVQ2TndpPcU4X65bup6WnGFWJdCA0GQOM+U10D+TS2lt8YnCWIBn2IfIze8hMHWQ8OYCmKbx2aNOE9HlTGR2LpBc0G2eZ0DoBFWV3YzAEePPYWnzz9AvCuHDYR8P+b9Lf9QIAGYXv5Uw3SkJoqKoBRTXidR8EIL/snYwOHePwG59mSe2nySq4fMI6oVDk61d/hgAcQG9IIb/sHXQ2/g5VnXoZSZpPciCeuSMD8Dly7c2vcfcdkb/y58EYWCBDgMdKRX4rwbCeNmfsf5Y/X8W5nfgCZural8S7KJF2xQocaV3GiNeOqoYpz2tlxGunbzgTg17hqsuGzxggzYSm6eh15dLryom0X87uBBS6+vNwe22k2tyU5nZQlteOogjGf4kdGnVQ11aF05WFogguXLGTJYVN02T7EGSlRjoAnql9cSLRqSE0oU55IxbJWd5Eh7Nw3oPvgG+AI29+Bo+7EYCy5R8lv/xdk5brbn6SlNQlWO1lhIIjBP2DpGatZ/N1W+ltf4aRoaOEQx7q9txH3R5YeeH3SM2sBSKdMAH0+tAZB/8JBUd487mbAMjIv2TqhSRJSkoyAD9Hp9dwGxO/Gau0QFlNXvKzeqhvr0z4kUL1uiC56X00dZXGvfYbQKdowMlgVdN0NHZVnLLEXP6SoNDYWUHTiWYs48ffM5hHU1cZuel9CKEQCBnwnGg7fqpjbVWsLD+Gpqkca6uaMid1eX4rSwpbGBp10OvKnsOyz73S3HaqihoJhfUca1ty2iBTgmUlDYyM2TjeUTnvZets+l00+F669gtTZiIBaDr8PQDWXf5rALwjzaSZM/GMNNN8+HukZW3AllrN6PAxgAnZUcaboJztRsnlfDP6uGHffwP/de4HJUlzQOYBnzsyAJ8F2axESkQVBS0EgkY6+qbu7JUoHCluqgqb0DSFroH4juipKBpVRY0UZ3cx7LHjOkvHurl2ppEap+tU2DOYh6LA8tI6Um1uDrdUT8i0YtAHKM1tp6WniPqO+P+6cDaqEqaioJnewRxQBCvK6tDrwvQPZxIK6ynJ6SA1ZYRdx9bE5UZtoOcVAHJL3nbG4BtAp08hHPIwPLgPgzGNruYnSM1az5DzTXQGG8s3/TeKMvVNcSgcmZ5i9uIZm3pU0qKyiyko+QueoWM0HvzKeR6VJM0BJUZpCBfhUJgyAD8LGXAnDgEoSuK2a44Xi3GMvAznGWtEE0WGY5C1Sw7iC5o40LiCQDCeKdQEtRVHyEodoLGrjDZnEcmSYqd7IA+3x8aK8jpWlB1Bpwp6BnMwGoJkpQ4gUJJitNis1EH0Oo3GrjL8QSNqucay4gZqSuujyzR1l+AaTZv3sgUDw/i93QCYLLk0H3mQvs6/AbDqou9htuajnAgWKlbeQ/2+r9B44Jtk5l3KQM/LdDb9DqM5i3DQg8fdhC116lFHQ2E9mqZQXdJAdUnDtOWqK7h2jo5QkhaGBx98kP/5n/+hu7ubFStW8N3vfpdLLjlzU63f/OY3fOMb36C+vp7U1FSuu+46vvnNb5KZmTmPpT5JBuAnyGA70SVHgDTfCrJ6CIV1ca9RPhtF0VheWodrNI299avi3vQkJ62fnPR+9jWspG8osdM1TsXjs7Hz6FouqNmNXhckP7MXf9CE05VNU3dpnG9upmcy+FlS1ITbY4s2s9nfuAqDLkiqbRi9LozbY5/UBGe++E4E3wBtdQ9PmNfV9Dh9nc+TU3w9FSs+RnbBFQwP7MXZvjWa+aT16A/ZdO3TdLc8xfG9X6F6/X1Y7WUIoSGEhrP9GVSdiZyiq3nj6HpMhskNwE/NyObseJ6+zhcIhULcfPPNsTloSZqpGDVBYZbb/N3vfsc999zDgw8+yJYtW/jRj37E9ddfz5EjRygpKZm0/Kuvvsrtt9/Od77zHW688UY6Ozu58847+dCHPsRTTz01V0cxK4s6AJdBdxKRld9TEBRk9dAzmJuQbb9VNUxhVjcZdhdmo5+99bVxDb7TbENkOlyU5EYGLErG4HucQGXAnUFueh+vHdoc7+LMysryo6iKxt7G2gnTg2FDQoyMaktdSnbh1fR1Pg+AOaWYcMhD0D9IT+sfAOhu/j0ZuReRlrWOqtWfITWjlvr9XwfAkVGLqhpZtu6LHH7j0+zd9j4ycrfgHWnB541k2DGac8gpuprRMRujY7azlqfu2CEGexuoqLkldgctSQnC7XZPeG4ymTCZJlcqfPvb3+aDH/wgH/rQhwD47ne/y7PPPstDDz3E1772tUnL79ixg7KyMj7xiU8AUF5ezkc+8hG+8Y1vxOAoZmZRBeAy4JYWGoMuSDCUmD2Al5fWkZfhZMxv5lBzDR7f1O1c50N+Zjcry+sIBPX0DOYmRAaW86UJFb0uHO9izEpuei8ZjiEONC6ft4GJZktRVJauvZfSmo+gN9jR6Uz4x3o58OpdBPz9mK0F6A12jr75Oezpy0lJXYojYyWrtvyAut3/iX+sF02EsNrLWX/lb+nreJ624z8j4OuP7sNkmX70Ti0coOXoDxnsfRUAe9rKmB2zJM2UoqjRJlhzvV2A4uLiCdO//OUvc999902YFggE2L17N//xH/8xYfo111zD66+/PuX2L7roIu699162bt3K9ddfj9Pp5Pe//z1vfetb5+4gZmnBB+Ay6JYWLoXO/nzK8tpwjaRNGh473qzmMdxeO28eXR/T/ZgMfkpz2+kezGXEO3mgGrNxjOVldXT05XO0dSkLoTmTomgUZPbMa+fR85WT5qS65Chd/bkJn6EFwGSO1MYPD+zH5+2ieuN/cXjHp1AUHVZ7OaAgRJiB7pfoavodBmMaqVlr6e96kY7jv6Rs+Z2oqoHckhvILroaIcKMjbaz/5V/wZa6bNr9dzU/Tk/rH6PP07I3AGMxOlpJSgzt7e04HCc7l09V+93f3084HCY3N3fC9NzcXHp6eqbc7kUXXcRvfvMbbr75Znw+H6FQiLe//e387//+79wewCwsyAD8mne/it4Qv9o2KTbkSJiTHe+oxGr2srbqAEdal9I9kB/vIgGRANGgC2I0BBkfxzQWlhQ2Up7fDkBpXgevHdqI97Sa9qLsbsJh3Yla7+QPviGSus5i8tPamxrvosxYVVETva5sDrdUk0yvw9Fd9xIOjgJQUHEzQb+L4YF9+MciX/RWewW5xW9FIOjv+jsAnU2PUbjkFgzGyOujqgbAgHtgHwD93X8nr/RtJwL5ybyjrbTX/wqLrRTvSBMZuVtQdUZkAH5+Lr5xGy8+eVG8i5HcVGXW7bVnvF3A4XBMCMDP5vTh64UQZxzS/siRI3ziE5/gS1/6Etdeey3d3d18+tOf5s477+Thhx+ecp1YW3x5X6SkJCCZvrPnjRAq+xpW0TWQx8ryOqpLjqNT4z8AS3FOJ1azj67+PGL1wqWYPZTltePxWdh9vJZA0EB1ST0GfSC6jMngpyi7i66BvIRsJ3+ugiEjvYPZVBa0YLOMxrs4M6LXhajvqCTZ3shrL30EgymSJaGr6XdY7aVseMtjbHjL/7F07Rew2EpxdjyDZ7iO6vX3R9dT1MlNw/LL/5EVm76Jqho5uutLU+4v6O+n7fCXKC2rwWqKtIddsvqzMTgySUpOWVlZ6HS6SbXdTqdzUq34uK997Wts2bKFT3/609TW1nLttdfy4IMP8rOf/Yzu7u4p14m1BVkDLi1EyfWlPZ+EUDnaupQRr42lRY1kp/XTNVAMTG6OMV/yM3sZ8abQ0Dl1Dd9smI0+DPogHp91QhBdW3kYf9DIgcYVjI7ZONhcw6qKI1xSu4PewWxGx1LIz+wlrKk0nxj8ZiE50rqM9cv2sa7qQFyGap+pTMcAoKezvwBfwBzv4syayZLNxqsep6v597QceZDWYz/Bai8nI/cisguvIrvwKjzuJg5tv4eWow9RseqT2NOWo9dPzuKiKAq2tGX4x3pxZKzGO9JMUYGd4pxuzEY/JkMAo8GPsuX9AOzdu5dDzVUYjA5kT3QpESTCUPRGo5H169fz/PPP8453vCM6/fnnn+emm26ach2v14tePzHk1eki3ydCxOe9tSBrwJ/7v4vjXQRJmmcKHX2FvH74AoZG0ijPbwGgsqApLkOSG/UBnENZaEIlNWUY5cSIkwD5mT1sXr6TsrxWakrrKMtrI8XsmbQNh9XNuqX7uKR2B5uX7+bKta9QVdQYad6iD5Bi9tLUVRbNIjHozuD1QxfQ1FWKI2WEioJWwpqOfQ2rCIYTs6Pq+QiF9ew9HskkUpHfGufSTC3TMcDK8qMANCbxTZCiqBRWvJtl6/8TgKM7P4+z/Zno/BRHBSs2fwuDKYOmg9/h2O4vMTp8fMpteYYjOb/dg/vZu+39ZJj+iskQ6S/R3Gnmz39+mt8/+Td++MMfsu2Vg+SVvC32ByhJSeZTn/oUP/3pT/nZz37G0aNH+eQnP0lbWxt33nknAJ/73Oe4/fbbo8vfeOONPPnkkzz00EM0NTXx2muv8YlPfIILLriAgoL4DGIna8ClpCCErAOfCV/AzMHm5TR0BVm7xkNBVjc5aT3UtS+hZzCH+TiLSwobMRsD+PxmKgtaKM9vIxA0ENZ06NRI1g6jIYjV3ILXZyUvw0lVUROD7jTanYU4h7IoyOyhpqwOz1gKh5qr8fisZDkGKc9vJTstkk0iGNbjPC2VYDBkpKWnlJae0pgfZyIIhIy0OQupKmrGOZSVEGn8xmXYXaxecojBkUjzjXjnf58LWfmX4am6nY76X1K//+t4R1sprf4XFEXFllrFys3fwjvSyvG9/8WB1+5i7aU/w2KbmNXBkbmGtZf9HCE0Wo7+kP97/E+su+xXCMXEkTc+jXekhYB/AIBN1z4dj8OUpDNKlKHob775ZgYGBrj//vvp7u5m5cqVbN26ldLSyGd/d3c3bW1t0eXf9773MTIywve//33+7d/+jbS0NK688kr++7//e06PYzYWbAD+6p8vkxlQFhz5E+xMhbXIW/vNoxsoy2tkVcVRstP6OdZWRTBkjOm+1VNGLE1NcRMM6enoz0enaGSn9WM1+04Mlx5pD6woGjlpfRTndLJ6yWEG3Gmk24bp7s/jaNvSaODm9jjoG86kOLsToyFIfUdFzI8lGbT0lJBuH2JZcQOukbToax9PjhQ3a5YcxDWSzpHWGq68ZCTeRZozpcs+gE5npvXYj+ls/C3ekWaWrb8PnS7SvMZqL6V8xcc5tP0T+LzdkwJwRVGw2svwjrQyPLCXkqXvA9XMkPMNhvp3kV/+j/R1PMeKzd9Cbzh7jnBJmneKEpth48/QefJsPvrRj/LRj350ynk///nPJ037+Mc/zsc//vFZ7ydW4v9JLUlSzPiDJg42rcDpclJdepwLV+zkUNPymKYsHBlLIRjSsaK8DoAjLUvp7I/8xNfaW0y6fQinK4vx2nghVHpdufS6cslO66eyoJnugdwJwXd02147R1qrY1b25KRQ11bFpuW7WVl+jP2NK4jn70UGfYDVlYcYGbOxv2EFen3y13yfrmjJrag6M82Hv4fLuYMdf72OjVc9gdEcqe3v63gWkzWf1Kx1U67vcTdwaPunsKQUklf6doQWovnIQzgyVlO+/C4qVnxsPg9HkqQ4WHifjKd49c+XxbsI0pxRZBuU89DrymH74Y2Mem2sqTpAhmMwJvsxG8eoKalnwJ3Bqwcv4OX9F0aDb4jcEPQM5qKJqTOS9A1lsePIRo60Vi+IJgvzxeu3cqiphpz0fpYUNhHPX4sq8lvR68IcaFxxxtd5ISgofyfrLv91NNvJzhfeRV/n3xjoeQVX305SM9egqpPruMJhP3V77sdkyWHVRf+L3mCnt30rY6MtlC//6BnTqElSIhhvghKLf4uN/IaTksbie3vOrUDQxN6GVQy601mz5BAW09znFE63D6HTaRxtXcaY34o/OHkQBSk2+oazON5RQXl+OxuW7cVq8salHOn2IQD8wYXfPMhiK2LjVY+jKJFA+/jeBzi264sIEaZoyXunXKf16I/xe3tYuvaL6A12wmEfbXWPkF10Lba06QfokSRpYVjwAbisBV8YZOvvuSGEyrG2pehULSYBmqqISIfZU7KeSPOntaeEXcfWYDIE2LxiF0uLG8hOc6LXBeepBIKwpjvRaXpxvGsNxjQ2XvV7copviE4L+gfwj/VOWtY/5qS75SlKln0Aqz3SWczv7SUYcJFdcOW8lVmSzpmqxu7fIrP4jlhKTiL6n3Se/EEj4bCK3Tr3A7j4gyYUBbJSB+Z829LMuEbT2H5kI+3OQnLTe1mz5Airyo/My01RTnofaTY3R1qqEQv460XTgnQ0/JZgYBgAgymNqtWfYfN1f0V3Iv93+/FfTMov3Nv+V1SdkdzStxPwD1K/7+vsf/UjANH1JElaHBZFJ0yZEWUhkA1Q5ooQKs6hLIpzOml3Fp5n1gyBxTRGpsNFhsNFduoArpFU+oYSJx3eYiSEQjCkx6APEQjqyUpzYdAHCZxnkyCryUswrCcYMpJi9pCT3k9OWh8mQ4CxgJk0m5u+oYxJ6SEXGv+Yk9ZjP6L12I9Yf+VvMVvzAdDpLazY9C1ajj6Ie3A/wwN7SMtaD4AQYZztW8kquJLAWB+Htt+NQFBYeQvZBW/BYiuK5yFJ0owoihKTfgqLse/DogjAJUmaqLm7hPxMJ6k2N4PujHPYgiAvw0lpbjuOlFE0TWHY46Cxq4zW3mLZgTKOHFY3y8vqsFk8tPYW09mfxwXVeyjJ6aChs/Kct5uX0cuqiqNomkIgZMBsDBAKqwwMZ6LThVEVjYNN1fQM5rLQb5jN1gJSHFV43PXs/vstrL7kJ9hSqwj4BujvfomcoutwDx5ibLQjGoAP9+/FP9ZLXsnbaDz4LfTGNFZd+F0MprT4Hswic+3Nr3H3HfEuhSQtogBc1oInN9n4ZG5ZTD4Awtq5ZanISh1gVcVRBtzp7GtYyaA7MfJPL2aqGqayoIXS3HZGxmy8cXQ9I147AJ39BZTkdjDitdPrymaqADkrtZ/8zF5ae4pxex0AKGiYjH4M+hD5mb0EggaaukoxGQMMjToYdKcvyEwnwYCbuj33EQqOYktdht5gQ29wkJ6zCau9HEVRqL34QQ5tv4cR12H2v/Iv1Gz8KgH/IF1Nv4tux5xyMgOQ23UYvcGB0ZKDe/AAVav/QwbfUvJRYtReOxa5xRPcovrGlEF4cluEv1DFTHFOF8MeO8OjjnNa32QIALCvYSXaOQbx0txQlTCF2d2U57Wh14do6Kygtbdowq8QDZ3lmI0+aiuP4PWZaXMW0dFXgBAqabYhSnPbyUkfIBDUk5fRh9OViaoK0mzD6HWR0UvDmsrR1qV0D+TF61Dnj9AY7t8DgOeUIeVbj/0IS0oxmfmXkZl/GSsv/C7Hdn0Jl3M7R3d+Prrcuit+QzjkJcVx8hcH70gLVns5zva/oih6MnK3zN/xSNIcSZSRMBeCRRWAS8lO1oPPFV/AhNnom3Kegka6fRidGkZRBKGwjqHR1Ak1nYPudISA/IzeCXm+pfmj1wUpzOqhJLcdkyFA90AuTV1ljAUsk5YVQuVg03I6nMMUZHezrLiB8vxWxvwW0mxuRrwpHG5eRvdALgVZPRRk9RAK62juLsHttRMMGfD6LIvmVw6DKY3N1z+De2A/Pm83fm8PQ/078bgbGfO009Hwazoafn1i2UwKK95DZ9NjAKiqEVU1YEmtmrBNRYFgYIjOxt+SV3YTeqN93o9LkqTEsTg+TU8ha8ElCXoGcyjK7qYwq3tCAK2qYWorDpOdNnGgnnBYZXAknf7hDPqHMxgLmHEOZU1aX4o9VQ2ztKiRgsweFEXQM5hDc3cpXv90WTQUXKNpuEbTaOkuIT+zF6tpjMPNy+gayGO8WUpnf4F8TQGdzkx6zqbocyE+zED3y7ic23H17SLoj2T6CfoH8Pv62HTtXxjsfY22uoc58OpHWbXlf6OdMwFyS27k8I5/R6e3UFx1+7wfjyTNCUWN0VD0sgmKJCUmsfh+nool10g6HX35LC1uZHQshazUAQbdGZTktpNuH2Jf/UqGPA6EUDAZ/GSlDpKVNsCyknpqFAiFdeh1YUbHZOq0+VaW10ZBVjct3aV09OUTCM0+s4nHl0JDZ0UMSrdwKYpKVsHlZBVcjhCCgH+A9uM/p7ftafq7/kYo6Gbp2i+QlrWeA6/dRcvRH1K9/j+j66dlraN6/X2oOhMGY2ocj0SSpESwIAPwnp4ePB7PGec//uOlfOCe3XO6T4M+0jzCZhkhaJDB4rk603k0GvwoimyCMpeOt1fisI5wQc1eACoK2gA42FRN3/DJNHKhsAGPz0Zrbwl6XZB02zBWixeL0Uf3QG5cyr44CQoyeyjLbaejr5Cm7rJ4F2jRUhQFkzmLnKLr6G17GnNKMe7Bgxx8/eMUV92B3pCCe/DQpPUy8y+NQ2klaQ6pSuRfLLa7yCzIAPyRRx7BbDafdZnNy+d2n6qqArVsrN6HpslRAM/V2c6jx3f211SanbCmZ099LdUl9ZiNPoY9dvIy+oAzX7+hsCESnA/PXzklAEFNyXGKcrrp7M+jsbMs3gWSAHv6CsqXf4zWYz9G0wIE/UMc3/sABmM6aTkX0N36R+xpy7GkFMqBdiRJmmBBBuDvf//7sdun7+Ayl7XgBr2gtlZj57E1BEOL705urpzpPBZmdZOT3hfHki1MwZCRg00ros/tFg8FWU56BmUb4ERhs4xSmttOQVYvR1qWyvbZCURRFAoq/pH0nE3sf/VOrPZyhAjjcTfQ1/E8fZ3Pg4jc0OoNDoqW/DOFlTfHudSSdO4URUWJQXvtWGwz0S3IADwvLw+HY/r0auM5cueC0SCAYUbH7ASCMgA/V2c6j/7ggGwHPg+8fisO60i8iyEBFtMYK8qOkW4fxh8wcrS1SgbfCcpiK6aw4mb6uv4OIkxh5XvILb4BvdGBZ7gBn7ebob6dtBz7EZn5l2G2LoJUjpIkndWCDMBnSmZEkaSJTAY/gZAx3sVY1HRqiJy0fpaVNBAM6dnfuJy+oSw5umiCK156O8VLJ2c3cWSsxJGxkozcC+nrfIGh/l3klbwtDiWUpDkg24DPmUUdgEuSdJKiaKTbh2juLj2v7Rj0AazmMYJBA4GQgVBYz0Ifmvx8RTLNDJCd1k+Gw4VOFThdWRxuWUYobIh38aQ5oDfYcGSspK/jORmAS0lLUVWUGIyEGYttJrpFH4DLWnBJiijK6kKnajhd2ee1neqS+hOdOSPCmkL3QB6+gAlFEZF/CNTxx6dMO+tz5ZR1TswTKGiagiZUNE1FEypCU9GEEn1+ct4p0048F0IlrKmIMyyj00duHPS6IKGQDk2ozMXNhKqEyc/sJTttALt1BLMxgCZgaCSNhs4K+oYyGZs2r/fs9zlX5ZdmT4gwAf8gPk8HbtdhHOkrpl9JkqQFa9EH4FJykCHDXIukdFxS2ERRdhf+oAmLaYzugdwZDOhydoPudPIy+jjYVIMmFDIdLnLS+hFCQaBE/goFIYgEzBOmKQgiIzeenKaiaZFp2mnbiATlGqoiUFUNnaqh6EKoyonHJ6arinbK38g6ygwuqvGsPJfU7ohm5YkE/FMF+CqappwSzJ8hqNeFyXQMYjb6CWsqmqZjzG8iFNaj14XIz+glP6MXMX7Vi/FXK/JciJOPT76SRPtInPp8/LFRH8SRMhotvxAKzqGsSGrP8XOOcmJfk/+KU7cX3a4SLUv07xTz9XoFcFCc00EwBGK8nKf/nWr/J+bPar8zOJ4z7f/sx3OmdSZOO9On1fDAPnyeDgB6Wp6SAbiUnBSFGX14nst2F5kFGYAPDQ3NKhXg079azT9+cMc0S5394jAYZI7q2BNnHD5diggEDROGjJ9KcXYHS4sb6R7MpjDLSUdfHnp9iIHhdNr7CjEZ/OdVhpExGwChsJ7+4UycrhyOti47r23OPXEyeFdPBPATgvTINKNRo7YWDjVXEw6fDN5PX27KaaqGThfCcNoyQii4RlIB5UTzHEAREz5hFMSEj5xoCKic8niK58qJbU14jsATMtDeVwgCdLoQSwqaSbcP4fGlMB6SjufZj/49UYYJf09bbnx/0XBUYdL2VJ0CLKc0tw0htCm3t9C+e4U4NUgHUBCaxtuv+BxCCH7zm98ghEBZaAcuSdKMLcgA/KGHHpo2D/jpLqk9v32O15RJseEPGtDpNC6pne5GaXEbHbOy48gGznbDaDWPoSiC1BPZToqye6LzSvM656wsmpbIwUWk5jIsVMJnuVcfz8rTN5S9YLIb2Syj6PWN7D22nKHRtJjvz2gQbFg3zKsHL5rmHJ75BuDUwJ4TTZAi805MH59/yo3MxBuK8XVncGMxYXun75dJNxwnb5bOfiMz2Ps6nqEjCBFgeHiYjOHj2NIS7cZUkqahKhCL9tqyE+bC8J73vGdGecBPd88X9p/zPisK5y5wkSYLa3r0ujD7GlYSDi++zhozYdCHWFlxhPK8Njr6z9yRMhAyENYUOvrz8QUsaNrcn08hVAZH0uZ8u9L5y3C4CGsKI15bvItymtObd4z/tzC0HB2kt30PjoxVuN1uBnpekQG4JC1iCzIALy8vn1Ee8NMNjrSf8z4LA/3nvK40PY8v0i7ZHzTi9sz+tV0sUro9VBa2MOTJmHq+2UNhVg9jfivtzpJ5Lp0UT6kpwxRmdVOY3cPQqIOwtiA//hNWQcU/4ep7k8He1wDoaPg1pdUfinOpJGmWZBvwOSOrEk/x6p8vi3cRpDMYD8BTzJ44lySx9Q7mAGAxTd1WPi/DicXk43j7kvkslhRnpbntXFCzlzT7ML2D2bQ7C+NdpEVBCI3etq34x5wYzZlUr//PCfN93u44lUySpHiTVSBSUohkijCTYvbGuygJy2wcY/WSQ3h9ZlwjacDEc5Vud1FR0ApAMCzf+otFht1FVVEjLT1F1HdUInMKzQ1n+zMYLdmkZa0/4zJjo+00HPgGAJl5l5KauYa0rA0M9e8CoL/rJYqW3DIv5ZWkuSDzgM8d+S18GpkXPHF5fFZsFhmATyU/o4easjqCISO76tYQ1iYP3mI78evBkZaljHhn30dCSnw6NYTdOorVPIbLnYYmFFZVHGHQnS6D7zlWv//rAGx520sTpocCI4wO13H4jX/HaMoCID17EwH/IM1HHkSIUHTZ1mM/Ir/sJnT6uc35Lkkxo6iRf7HY7iIjA/ApyCA8MXnGrGSnybb2pyvNbaOqqInugVyOtVUR1vQnsndM1Nmfz7KShmh2BmnhUBSNyoIWinM60evCQCQLTaTDrcrB5hpk8B0bAb8LoykdAO9IC/tfvRMt7Dsxrx9FNVC5+tOYzFkEA26aD3+fvs7nouu31/+aspoPx6XskiTFjwzApaTh8VkpMflOjOh39lzXi0V2aj9Li5to7i6mobOCswVZSwqbT+SgTpu38klzz2wcoyi7G6vZS0t3Cb6AidVLDuOwjtDaW0zPYA6+gIninE7MRj+tvUUEQ8Z4F3vBsaUuY3S4jgOv/itFS96Lweig9dhPMJqzWFL7aUaHjmIwZZBdcCWKGvmqNRgdVK35HLbUpah6M40Hvkln46Pkl92EyZIb5yNaXK69+TX+/uTl8S5G8lGU2KQMXISdMGUAfgayFjzxjI6loCiRPNajY4mWQm3+KYrG8vJjAGSnDZCT3h8dol2vU4BqtqzcgRBhIIwm9DR2lZ4YfEVKLgKTwU9W6iDVJfWENB06VSM1ZQRF0UAo7Kpbw7AnNbpGc3dZ/Iq7CNjSIgG4JaWIxoPfjE5ftv4/URQVRTXg7HgWl/NNrLYSckveitGciRb2kZK6FFvqEkZcR3C2b+XY7vtYffFDcTwaSZLmmwzA54r8VT/mTs2EIgNwQEBbbxFGffCUYd4jw4HodJHahI7+AoJBBb0uSHl+O6oqL9RktKLsGAVZvQC0Ows43lFJduoAy0rqcY2kUde+hEDQFOdSLi5CREZwGu9QOa5u95dPPFJJy16Pf6yHge4XESKM3mCjo+FRggEXVns53pFmAEaHjuIePIQjY+V8HoIkzZqiqCgxaK8di20mOhmAn4WsBU8sobABf9BAisULrniXJv4E6hlrOcdHcGztKUHTwly8ajsQ+RVBSi5GfYDc9D66+nPp6s/HNRoZxr7XlUOvKyfexVu0KlZ8nNzi6/F5e1BVA0ZzFoqiQxMhdDoL5pRCdLrITdG+lz9Ie/0vAIWc4utJy1rP8b3/NWF7B1//2KQOnZIkLVwyAJ8jQnZwmheesRSZinAWFEWjKLsTgz7M/sblOGXAlhQUNNIdQzisI+Rn9hLWdBzvqJRtuROIqjNhT1+BPX3FtMsGfIMAWO1lVK3+DAAdDb/CYivDPXgALRwgHBrF5+3BbM2Labkl6byoMWoDvgiHol98df6zJAfnSSwen1UG4LOwefkulhQ20zOQQ/9QZryLI81ApmOAzSt2sX7pAcry2vAHTeyqWyOD7yQVCnkJBiI/2Zms+dHplpQSQgE3F1z9JJuu+QNZhVdzaMe/IYRsJiZJi4GsAZeSimfMSmFWNwoaQt4/TkugsP3wRtnxMuEJMhwuynLbyUx1MTiSyhtH1+H22JHpA5NbKOCOPranLY8+9ow0kZa1AQBF1ePqfZ1wyIPP04HFVjzv5ZSkGZF5wOeMDMBnQLYFTxweXwqqKrCYfHj9cvCK6ThdWTL4TlgCgz5IbnofxTmd2CxeRrwp7GtYQd9QFjLwXhhMlhxKqz+CwZRGTtG1aFqI9vpf4PN0kFb9kehy2YVvoaf1T4x52mUALiUuRYlNykCZhlCSEtvoeCYUi1cG4GcV+RnbpA/EuRxShCA1xU1W6gCOlBEsRh9mox+dTkMIcA5lcax1abSDpZRYQsFRFEWHTm+ZNC/gG8A/1oMtrWbKTA6KokaHm/d5uzm+5wFGho9RsuyDZORtiS7nyKilp/VPjA7VkZF7UewORpKkhCAD8BmSteCJIRA0EgzpSDF76CMr3sVJWLnpfYCJHtnpMu4MuiAryo+SnTaIP2jA7XHQ787EFzDhC5hwexz4AuZ4F1M6i6O7voB7YB/ZRddSVPkeLLYylBM1doff+AzekUbScy5k+QVfO+M2hvp2cWz3l9EbHdRe9L8z6rwpSQlHVSP/YrHdRUYG4FKSUfD4ZCaUsynM6qKmtAGolaNexpnD6qa28jA6VWN/wwqcsmlJUipe8l4OD+yjr+NZ+jqeBSAteyOh4CjekUbs6StwObfjcr6BxVaCyZIXDdABBrpfpm7vA6RlrmPpui+iN0wex2BstB2INFmRJGnhkwH4LMha8MTg8VmxmT3xLkZCMht91JQep7u/gNWADPbiR1E0Vi85hD9oYn/DCvxBWcudrNKyN7D28l/S0/IU3S1PATDUtxOAnOIbKK3+EHtevI0jb34WAKM5h7Ts9Vjt5fi93XS3/JGsgsuoWvN5VNUw5T46m34HQIpjyTwckQRw8Y3bZKaz2ZKdMOeMDMDniEwcNX80TUVVtXgXIyEVZncRCuuo76rgOkbjXZxFLT+jF7MxwJ7jq2XwvQBYbSVUrLyb8hUfZ8R1mP6uF+nvfgln+1ZGBg9SVnMnVns5oaCbof7duAf209/1Ijq9heKlt1NcdRuKopty20ILoYV9AKSkygBckhaDWd1yPPTQQ9TW1uJwOHA4HFx44YX89a9/jc5/3/veh6IoE/5t3rx5wjbq6urYsmULRUVF3H///RPmlZVF2tXt2LFjwvR77rmHyy+/fJaHFhvybjn+FEWgicV3tzwTJn0Ar9+Kpk39RS/FkmBpcQMXrniTi1a+wYryOpmFZgFSFBVHxioqVn6CjVc9zsrN32HM007jwW9htuaTkXsRFSs+zppLf8rm6/7KxquepGTp+6YMvkPBUZqPPMjrW68CONF0Rb53F7uEjrXGB+KJxb9FZlY14EVFRXz9619nyZLIHfovfvELbrrpJvbu3cuKFZEOJddddx2PPPJIdB2jceLgEXfddRe33XYbGzdu5M477+Qtb3kLW7ac7AluNpv57Gc/y7ZtSdbUQyy+iydeVEVD02QAPhV/0Ei2cSDexViUslIHKc3toGsgl2DQQPOYjd7B7Jju06ALkuFwUZDVjc0ySihsQNNUNKES1lQ0TXfir0pY00X/hseXCetOWfbUZSLrBsO6EwMALe7PNyHCUwbGiqLDYM5EUXSkZW3AYEo7bf7k8+YePEjDgf8hp+g6Ar5+uluejM5beeF35rzsUvKRsdbiMKsA/MYbb5zw/Ctf+QoPPfQQO3bsiF4UJpOJvLwzD6U7NDTE2rVrqa2tpaCggOHh4QnzP/KRj/DQQw+xdetWbrjhhtkUb97ItuDxpaoamrzhmUSvC5Kf2YtrJDXeRVmU0u1DjPlNHG6uiel+9LoQRdld5KY7sVtHURRwe2y09pRgNvpRVQ2dGj7xV8OgD6JTtchz5eR0VQ2jU7Wzpt91jTjQhEp9RyV6XQjXSHpMjy0RedxN7Hv5A2TmX86ydV+alGqw7dhPMVlyWbbhP89aez2+nXGdjY+x9vJfkJq1DntaDUazHKlWikjoWEtRYtQGfPF9p59zG/BwOMzjjz+Ox+PhwgsvjE5/6aWXyMnJIS0tjcsuu4yvfOUr5OSc7NV9//33c/XVVzM2Nsbb3vY2rr322gnbLSsr48477+Rzn/sc1113HeoiTE0jnZ2qCFkDfpr8zB6WFjWiKBoNnRXxLs6iZLN4GB2bnN3ifBn0ATLsQ2Q4XDisI9gso4BCryubNmcRg+6082hjLlAUcTIgV7QJAXxeRi+FWd1sXr4bIeBg03J6F1lqy/FBcQa6X+LAqz1Ub3gAkyXyy8bwwAGG+neTV/p2dLqzvwYBX19keynFVG94AKu9DIDMvItjV3gp6SVcrCUH4pkzsw7ADx48yIUXXojP58Nms/HUU0+xfHlkeN3rr7+ef/qnf6K0tJTm5ma++MUvcuWVV7J7925MJhMAN9xwA319fbjdbrKzp/559gtf+AKPPPIIv/nNb7jttttmfVDBYJBgMDjr9WbjxScv4tqbX4s+1+siF4/RILtjno/x83e286jXC4TQyXN9QkFWF8uKG/H6LQyP2Ckv6DxxPWZRU9JEKDz1eZp6qjKThU6bFVnHYvaSnTqIpiloQkWIyLzxvwjlxDqRv0Iopz0+9S+IU9Y9df6EdU/Zhzh1HxOWVyA6P/I3ENJh0IURQo1sj4n7Hd+OTlWAVMoL2gmFlFPKdXp5FdJsIwx7HZTkdaOcCGwVBChEH0e+Y8bnRaYz/nj8bCgCnRoGBPYUD3ZLJOOPx2fF5U6lbziXvqFs/EFT9BU4v/eCAugAHWEB4TAEw5E57U4rXr+DcFhPVtoAtZV1HGzSMTiSMeOtz+Q9ndj0XHjto+x7+YOEA60cfv12Csr/kTFPBy7nDtIzl1BUfjW+kQOEAm7Ssjei6oyTtpJbeAG573jxlCmzOx/Jfx4Tw+nnMdaxwulmur9kiLWk86MIIWb1bg4EArS1tTE0NMQTTzzBT3/6U7Zt2xa9ME7V3d1NaWkpjz32GO985zun3XZZWRn33HMP99xzD/fffz+PPPIIdXV1fOYzn2Hfvn289NJLZ13f7XaTmprKo48+itUqR0mUJEmSJClxeL1ebr31VoaHh3E4HGdcLtFirfH4qvd338JhnTwi7Plye8fIvfnfpj0vC8msa8CNRmO0Y8CGDRvYuXMn/+///T9+9KMfTVo2Pz+f0tJS6uvrZ12wT33qUzz44IM8+OCDs173mmuumbcXcLwWvKakiWuuyOShRx0Egovvp5S5YjQI/vVW91nP49qq/fj8Zo62LZvn0iUWnRqiMLuL8rw2egdzONa+NDpvJucxVmXaWL2PQEjPnuNr5m2/s6EqYS6p3U59ZwVd/QVnXXZm53G8Xl8jUoc9Xp89e3pdkJXlR0lNcbOvYRXDnsRqz59iHuWCmr3sqlvNiHdmn7HxuhZjIRgYxuXcgWe4Ee9oKwajA6utFLOtGKuthJGhOpoPfw9H5lrcA3sB2HDl79Ab7ee974V0HuPp9PP47O+2TL/SHHK73TNaLhliLen8nHcecCEEfr9/ynkDAwO0t7eTn58/6+3abDa++MUvct99903qkDAdg8GAwTD1YAdzbfyDcPxn/kBQkR+Oc+Bs51GIMMEQi/o8m41jrKs6gNnoo92ZT31HBWFt8vmY/+vRwOiYkVBYl8Cvjx6314LVODLjMp79PI5PP79+CWajj9WVBzAaAuw8toqh0bTz2l4sKBjQNI0lBfX4g0Y0oUazrghNIXziuRDqiXkKqk4FbGTYnfgDJ6drQsXrs05oSpPwlDTScq8jLXfyrBHXEY4f+BGKYqa343UAVl/yEzTFQWAOWznI75i5MX4e5ytWGHeu+0uYWEu2AZ8zswrAP//5z3P99ddTXFzMyMgIjz32GC+99BLPPPMMo6Oj3HfffbzrXe8iPz+flpYWPv/5z5OVlcU73vGOcyrchz/8Yb7zne/w29/+lk2bNp3TNuaLbJU3f1RFW9R5wFUlzNLiRnRqmNcPX8CYf+5/DjwfFuMYzqHYpt87X26PndSUmdVEzZeqokb0uhA7j67D60/MJnT+oImWnqJIthXlRJYVXQhV1VBPdOCM/BWoioaiaBj0AKuoKT2Opk0cQCsU1nGwaTn9w4mRASQU8qLTmSdlOplKMDBMR8OjjA7XEfAN4Pd2ozOkEPQPAbDhLf8nh5VPAok4GqaMtRaHWQXgvb293HbbbXR3d5OamkptbS3PPPNMtKftwYMH+eUvf8nQ0BD5+flcccUV/O53v8NuP7ef3wwGAw888AC33nrrOa0/H2RKwvmnLPIsKLWVh8lOG6S+ozzhgm8FDbPRz5g/sUd+dHvsFGZ1o6rhhBi0SK8LkpPWT9dAHt4Ee00nUqjvmN1IjUaDYM2aYV7at4VQSESDdJ2qUVXUyJolB6lrX0K7s5B45xvf+dw7sNhKWHPpTwAY7H2druYnMBjTUVQdQgughYMILciYtxP/mBODwYE9YxWWlCJczu0AbLz6SYymmXdUlaRTJXSsJYeinzOzCsAffvjhM86zWCw8++yz51WYlpaWSdNuueUWbrnllvPa7vxYfD+fxMtirgE36v0ngu8KWnpK4l2cSUxGP6oqEu7G4HRujx1FAbt1lOHR+Lez1jSV/uFMirK7STF7qWtfwoj3/NsNJxIhVMKaQviUSvD9jSupKmqiuqQhetwiju9tTfPjcdfjHjyEy7mDjoZfY09feSKFoIaiGlAVA4pqwJZajcVahKtvBwPdJ7ObrL/iURl8S+dFxlqLw3m3AZciteAf/ngjAGX5LbR0FRAIJVG7xiSzmAfiSbF4AXAOJcZP9qezmHwACV8DPupLIayppFrdiRGACx37G1eS4RhkWXEDm2p209RdSlNXGQv75l6hvqMSj89KTclxrKYxDjQtJxSe33a541SdGS3s4+DrHwNUSqs/TGHlLVOOaAmRdrmjw3W0HfspwwP7WHnR/8OccvaOvZKU1BQVYjE+i6wBl85V72AWICjO7qIos5269iV09uezsL8842MxD8RjNkYCXF+CBrhW0xhCwFggMcs3TgiVEa8NR8pIvIsywaA7gx2HN1CW30ZlQQupKW6Ot1fi8c39AD+JpKs/nzG/mdWVh9lYvZd99asYC8zvryjhsB8t7MNkyWVJ7acxpxRhtp55pEGIDDVvT6tmxeZvzlMpJSnOZCfMObM4o5gY+P3Prwdg++GNdA/ksrzsOGuWHMKgD0xYLsXsYXnZMTLsg/Eo5oKgKFp0AJTFJi+jj9ExK5qIf7vlqVhMPnwBU1ybEcyU22NPuAAcQKDS3F3GnvpabBYvF63cxSW1r7Oi/Oikz5OFxDWSzptH16EqggtqdpNmG5q3ffd3vcSeFyMDkWQVXElq1vppg29JkqTzIWvA51gobOBo2zL6hjNZUVbHRSveZMCdgcXkIxTWY7eOYDIEKczqoXsgh0PNNcha8tmJNEFJ/ABvLuVl9FKU3UW6fZh9DSviXZwzspjGEr799zi3x05Jbid6XTBuTR7OZtCdwasHN5GdOoDdOkJRThcqgoPNkwfiWCi8fitvHl1H7ZJDrF+6nyOty+geiF0gHAp5aTr0/+jreJaM3C2Ubf42FltRzPYnSUlPdsKcMzIAj5H+4Sy2H3ZQUdCC3TqK12fBoA/hD5g40rIMgy7EivJjeH1WmrrL4l3cJCLQqYurCUpW6gCrKo7SP5zOoeZq+oay4l2kM7IYfYyOpcS7GDMyfKKTo8M6yuBIepxLMzUhVJxD2TiHsgmGDFQVNZHSPbqgm6QEwwb2HF9NTclxVpYfw2ry0thVzlxXVAz2vk7jwe8QCo5SteZzZBdec8a23pIkSXNNBuAxFAgZOda29Izz7dZRSvPaaeouRdaCz4yiRDKuL5ZOmIqiUZLbzpjfxN76WhL9OrGYfPQlSE7n6Xh9VkJhHY4Ud8IG4Kfq6CugMLubFeV17Dy6FrGAWxAKoXKkdRkev5WqwiZSzGMcaqmes5SRzo7nqd/3VdJzNlGx8m7M1tkPYCJJi5JsAz5nFu4neJzMZljbfncGel2YC2r2sKriMCvKjlKe30KK2YMc2mdq4wF4MrQxPl82yyibanaTbhvmcEs1iR5869QQRkMwaZqggJKw7cCnogkdR1qW4bCOUJLbEe/izAOF1p4S9jeuICt1gA3L9mE0TD0S4GyMuI7ScOAb5BRdS83Gr8rgW5KkuJA14HE06E7naGsV6bZhDPogqiFMTno/Swpb8PgsuD12VCUysMmwx0FjVzmhsJ502xDp9iHCmg5NKBj1QXwBE539Cz/9lapEkggv9CYoqhJmbdUBgiEDbxxdz+hY4jc5SJYUhKca9tjJz3TGuxgzNuxJpa23iMrCZvqGshJ2xMy51DeUzc46M2uWHOSC6j3sa1h1Tu+Huj0PIESYkcFD2FKXUrnqUzMa8VJa+BJxNMyEpcYoDWEstpngZAAeAzMfHVOho6+Qjr7Ck1MUjUyHi+y0flLMXjSh4vFbKcjqJie9j0F3OvmZvQTDelRFnBgVUkGnC9M1kLfga4ZV9UQAvsCPM8XixWwMUNe+JCmCb4h0wASSqAYc3F475fntGPX+pMnd39BVTnbaAMvLjrGrbi2J/svIXBjx2nnz6HrWLDnIxuo9J4avn11fiP6uv514pFJ78UOoOuPcF1SSJGmGZACeYISIjIjXf1o72kZjOUsKm0izDU8aoMNmGeXCFbuoLq6nsbuMQHBiIKEq4RMBa/J/UauLoA24zTJKVuoAAA7rCE5XTpxLNDORTD8qgVDiZRQ5E7fHAYAjZYT+4eQIwDVNx+GWZWys3seqiiMcbErcrDhzyR80sbNuLSvLj7JmySGOd1TS1lvEbD/XLLYSTJbs2BRSkhY4oSiIGLTXjsU2E50MwGNk5rXgM+MLmDl0hvRjo2M2jrdXUJ7fSl6mkzeOrMfrt6JTQywtbqQwqxvXaCp7jq+etobcZhnFkTKC05U1p6nZ9LogJoN/UvYGu3WETMcggyPpuD3TD7290JugpNmG2Fi9j7Cm0DuYTbszeVKinUxBmDwfpL6AiUDQQGrKyKxrVONpaDSNwZFU8jL6CATrOd5RueB//YLIzceBxhUsKWxiWXEjKWYvx9qqpj12/9jJZka5xdfFupiStHApSozSECbP98ZckQH4AtHaW0Jnfz5XrH2NvMxe3B4Hy4rrMRoCtPYWU5rbzurKw7Q7CxhwZwICvS5Ebnofwx4Ho2M2UsweNtXsRlUFJTkd7K2vxR80UpTdRabDRUNnOR7f2VPMqUoYu3UUj89KWNNRnN1JVtoA6bZhVFXQ7iygoy8fTdORmTpIZUELBn0IaGZo1EFb79nTjS30JiihcOQtebRlGd2DyTUQiNXkS6r23xEKbm/ydMQ81e66NRRld7GsuIFUm5sDjcvxzfPokfGh0NAZGb5+eelxLMYRXnxtmL6evYy4jiJEGEXRoai6yF9FJegfiq6dVXBl/IouSZJ0ggzAY2iua8GnEwobGBp1UFnQCoBrxMGe+lrG/FZGx1IozW1n3dKDdPXnkmL2kmqLBB1uj42m7jJWVRwmEDKyr2El65fu49LV2xnzm6Od61RV40DjcsLaxMtGVcLkZjjJSRsg0zGITqdNmN8/nEF9RwVW8xjFOV0U53QBoGkKfUNZHGldSpptmIqCVtZUHQRqsZi8BIKTg/3xGvCFOhKmx2dF0xT0ulC8izJrFuMY/e7kSEF4qmGPneLsLiKZh5Lpuor0IRn2OKitPMzm5bs51FwzqfnaQtX+/9m77/A2q+uB499XW95775k4jrMTMgkjIYwwWyhhlj3LaCkEAoTQMlugUEihUCgbyo9RIAUCISEkQPZ2HDvee1u2Ze3394djE5NlO5K17ud59GBL7zgSjnR033PPrQ+hbF8FE/NVlO1+C4cUTUjEWBRKLch2ZNmOLDuQZTu6gEQyxt5OUGgOak2ou0MXBO8lFuJxGpGA+5jNReMIDTJgs6voNAbRl1DUtcRR1xJHSmwVabFVdJsC2F2eS2psFXaHgriIBpQKmfV7x2Oy6Fm3axqRIa2EBhlo6wxDrbSRm1LMzLE/0dweSXVzAkaTnqjQVtLjKwjUGenoDqG0LpXWznACtUZUShsGYzAd3T9/4O2vSSdAZ0SpcNDZE4jV1jsRqrkjiuaOSOIjWygogEk529hdlkVje/SAy8v9fcB9sgRFJj2uEoVCpq3L25IEGb1XjoD3roipSbCi05i8cgS50xjMT3smkZ++l3FZO9lTNsrrrp4Mheyw0VC1gsqif2G39dDY/Bvypr+ORucfXzwEQfANIgF3sZEeBXfIStqOsqhIZUMylQ3J/b8rJAejU4sBKKzI7k9ArDYN9a1x1B/0Qd5iiCAltoqo0Fbio+pRHBgsbO8K4YfdkwfUd/dNbvslq109ICEfSDpQHtNBe1coBZmFWGzFVDUmUtMUj9mqO6gExZtGKo9NpbQyNmMPUaFt7K9Npavn2PXwnkSrNqNQyF6agP88EdMbE3Dovfq1vWQMo1P3kZ+xF5NVe9T3AW/VbShh39Y/Y+wsIzrpNFJzrxETKgVhBIlJmM4jEnA/V92UgNWmQqOyUtN09AUpTBYd+6qyKa52kJlQRnRYCzv257lkWexdZWPQqLpJiKwnPa6SzIQKTBYtxgMJnsPhW/9Y0+MriQptY/v+PK/penKwn3uAe18Ca7Fp6DFrCQ30no4zhyPTu3pkoN5IbnIJW/YVeE1rxcFoa/yJvZsfRB+YRMGsfxAcNsrdIQmCIAybSMBHwEiPgg+NRENb7JD2kGUFJTWZlNRkuiimXl09QeyrzqKsPoWwoA7CggyEBbbTY9YwOnUfZXVpBOm7kZFQKuyYzDpaO8MOqVH3Bn1fKOx274sdvHMRnoMZjMGEBHjfRMxDSeytyGZCzg6mj9nInopcmtq9f4S4s30vezc9QGjURHInPoBS5X1f9ATBJ4gacKfxzk97wa9YbRqa2qP7E4nosGZGpxYxdfTW/m0cDgmFQqa5I5wd+8d4XRIeEtgF4IW137302h7MFg0OWenuUIbF0B1MWlwl3jcR81CdPcH8sHsKean7GJ+1m5rmOPZWZuNweOf/G5OxnsKNiwkIySB30lKUSt8Z1Rc8h1gNUxhp3pWleDHPHgX3Lk3tUbR1hhIZ0kp7VxgWmxpZlg700N5OSGCn19W/1rfGEBXaSlpsFaV1ae4OZ8j0WlN/eZA3MnQHo1bZCdAZMR6j1aY3sNo0bN8/hoTIenJTitGqzWwrGet1vcJt1i4KN96DQqFl9JRHRPItCO4mSa7p2e2HNeDe9W4sCAfY7Goa2mIxW7UHkgoJk6U3AfTGf8Z1LXE0d0QQEdLm7lCGRa/p8cr67z4GY++k11CfKEPpI1HbEs/2krFEhLSREFnv7oAGraerGkPrDvZufhCzqZm8qY+j0XrXl2pBEISjESPggg85kHofaFXoXWSC9V00tHlnvW6A1kSrl111OJjNrqbbpCcksNPnWvi1dobTY9YTHdZCXWusx5eiOOwWtqy+tP/3vKlPEBCc6saIBEHop1D03lxxXD/jf8/YjUR9mWvJB/JuCW9MwEGjtmD0wlFkhcKOVmPx6hFw6C1DCQk0uDsMlyitTSMypJVJOdtRSHZ3h3NUzbWr+n/WByYTFj3FjdEIgnCwvjaErrj5G5GACz5DPjACLnnlCLhEiyGcjPgKNCqzu4MZEr3Guzug9DF0BxMc0IUkOY69sZepb41lY9EEQgI6SY2rcnc4R6ULTOr/OTx2BpIffjALguD7RAI+wsQouOv0LU/vrR/XxdWZaNRWQgK9qw5Zr+0BvD8B7zCGoFTIBOm73R2KSxi6Q6huSiAtrorwYM+daxAcPga1tndVy+SsS4+xtSAII6qvDaErbn7G/56x4Ltkb64BhyB9byvCjiOsIuqp9FoTdoeE2erdHSo6jUE4ZHykH/jhldSk09EdwsTsHeQkF6NVm9wd0gAWUwtFW5ZiNbeQPe4eVBrvWhFWEARhsMQkTDcQLQldoy/tVkh2JMkxuFrwIwyXD2bfgVfGB5/0D9jtoC8LUaEtdBoDsdo0gz6WJ9Br+zqgeOu1h14Oh5LunkBCAjupaXZ3NK5hd6jYWjyWtLhKUmOrSY6uZVvJWFoMEW6LyeGw0tm6C0PbbmpL30OSlOROfJCohJPcFpMgCIcnSwpkF4xWu+KYnk4k4ILPkB3Q3aOjIHMvsNfd4QyZwwFVjYnuDmPIArSm/haQ3s7QHUyoj07E7CPLCsrq0qhsSGJc5m7y0vbyw+4p2OzqEY+lteFH9m19GLutG4VSR1T8iaTl3YhaEzbisQiCIIwkkYC7iRgFdwFJIlBvorIhAcOBMo6B49LHHqGVB+wwiO0Pe+fh9zvyGLkEMiRE1ZEcU0tbZzhNHVHHPLen0GlMXruC5y91dIcQH1WPWmXxuisRQ2V3qNhdnsv0MRvJSixjb2XOiMdQV/YBusBEsgruIjAkA0ny7BaJgm8Tq2EOgliIx2lEAi74nPauMBraYtwdxpC1GMKZVfAjESFtXpSAywRoe6ht8Y3e2Y3tUYxKKSYuvJGqpqRj7+DlzFYdVY1JJMfUsLcym5EqI7JZuynb/RztzZvIKriLoNDsETmvIAiCp/C/ohsPIr5pu4Z3TsGEyNA2tGorTR2R7g5l0DQqK0qlw+s7oPSx2jQYjMGEBvl2GcrBWgzhqFU2l08+3b/raYq3PUp1yVtsXfNbmuvWkDXubmKSz3DpeQVBcB4ZRX8duFNvw0hHX3jhBdLT09HpdEyaNIm1a9cedXuz2cx9991HamoqWq2WzMxM/vWvfw33pThuYgRc8BnefgUrWN+Fza6guyfA3aEM2s8tCL17EZ6DtXREkBZXeaAfu2+XoUBv2Y3NriQipA2D0XUdeJqqV9LT0wOSgsjYWaSNvgFdYILLzicIggt4SAnKe++9x+23384LL7zAzJkzefHFFzn99NPZs2cPKSkph93nwgsvpKGhgVdeeYWsrCwaGxux2WzOiH5YxAi4m4lRcKFPa2cYKqWD1Nhqd4cyaHqtbyzCc7DKxkQcskRavGcvWOMssqygpSOc+MgGXHn9SB+UTGjkRGac8TWjJi8TybcgCMP21FNPcfXVV3PNNdcwevRonnnmGZKTk1m+fPlht//iiy9Ys2YNK1as4NRTTyUtLY2pU6cyY8aMEY78ZyIBF3yItxaf9EqIrAegvtV76tf12h4sVjV2h+9cTLPZ1VQ2JJMUXYNW7V2rkg5XZWMSQXojMeFNTjmeLMuYumvpNuynpvQ/AJh7mkjLuxHJD9uNCYLPkCQXLcTTOwJuMBgG3MzmQ9+DLRYLmzdvZv78+QPunz9/PuvXrz9s2P/973+ZPHkyTzzxBImJieTk5PCHP/yh96qcm/jOp6YXEx1RnOwIXUg8Xbept/RE9qL49VqTT41+96lsTCIltpqUmGrAWybEDl97VxjNHeEUZOyhqrGDoqosBjMh02btxGJuQ6uLRqnqLUPq6aqmcNMSerrKAQgMDAPmM27Wi0gq7/lyKQjCyEtOTh7w+4MPPsjSpUsH3Nfc3Izdbic2NnbA/bGxsdTX1x/2uKWlpXz//ffodDo++ugjmpubuemmm2htbXVbHbhIwAXBQ1Q2JJGZWE54SDudPd6xAuDPi/D4FptdRXl9MtlJlfhDAg6wrWQsKTE15CTvx2jSH7MLTEPlCkp2PNH/u0YXg1YfQ2fbHnQBcYye8ghKpZ6ImFGAGa0+GovVxU9CEASXkiUJ2QU14H3HrKqqIiTk57koWu2RV1iWfhGHLMuH3NfH4XAgSRJvvfUWoaG9bXOfeuopfvWrX/H888+j14/855i4FigIHkJGgcmsJTqsGUlyuDucQdFrTBh9ZBGeX6ppjnd3CCNKlhVUNCRT1ZhAdlJp/wTbg3Ub9lO46X5s1i70wakDHouInY5aG07m2DsYN+dlImJnEBo1AaXSN/8+BEFwvpCQkAG3wyXgUVFRKJXKQ0a7GxsbDxkV7xMfH09iYmJ/8g0wevRoZFmmuto9865EAu4hxGRMAaCwMoewQAPTx2wkQGt0dzhHJUkOdBozJh8sQYGfWxL6m+LqDMw2DXlpe/nlvIrOtkJa69ey7btr2L/jKTLH3kn+CU8DEBY9mdGTHyYudSEqlfd08hGEg4ly0GNwSf33gdsgaTQaJk2axMqVKwfcv3LlyiNOqpw5cya1tbV0dXX137dv3z4UCgVJSe5Z80Ek4ILgQdo6w/lxz2RkWWJC9g48eWKpXmNCksDogyUofSSp9/VXKOxujmTk2B0q9lVlERHcQbC+a8BjAQdGvc099Rg791O571Uaqr8AQB/o+wsXCYLgGe68805efvll/vWvf1FYWMgdd9xBZWUlN9xwAwCLFy/m8ssv799+0aJFREZG8tvf/pY9e/bw3Xffcdddd3HVVVe5pfwERALuUcQouHN4bso6ON2mQEpr0wjQmdCqLe4O54h+7gHumyPgAJX1vROCUuMq3RzJyGruiMBiVRMbMbArSnB4PsHh+QDEJC3Aau6gpe47MsfeSUBwujtCFQRhBMlILrsNxUUXXcQzzzzDsmXLGD9+PN999x0rVqwgNbV3kKCuro7Kyp/ft4OCgli5ciXt7e1MnjyZSy65hIULF/Lss8869fUZCjEJU/AZ3tM75Oi0ajMZCeX0mLWYrZ67EIxea8LhkDBbjjxJxptJkoPEmFoghc4u1y1Q44lkWYHJqkWlHLhIhSRJjJr0EBu/vgCr1cCkk99CpQpCpfG/Uh1B8Ed9K1e64rhDddNNN3HTTTcd9rHXXnvtkPtGjRp1SNmKO4kRcA8jRsH9W3BAJ1NGbUGpsLNl3zg8+WuFXmvCZNEOawlhb5CZUE5YYAcAzYZIN0cz8swWDdFhLWjVvYstybJMa8N6Snc/B0jEJp+OLiBeJN+CIAjD4JufnIJ/kry9+KQ36ZNlBRv3TsBo9uyJbL7agrCXTHxkPbV+1gnlYIUVuYDMrLE/MTZjNwrTl+zddB9d7YVkj7ubyLjZ7g5REISR5gGTMH2F/z1jLyBGwf2TTmMiNLCDpo4IzFbPr6vWa030+GgLwpDATnQaC00d/jfy3cds1fLD7ikUV2cQpO/m1Nk6snMnMvmU94hJXuDu8ARBELyaqAEXfIbNrsJsVZOVWEanMRiTFyWHoUEd5KcVYnOoKKtLPfYObiej1/RQ3+qbKxvGRTRgtqpp7woFDO4Ox21sdjUVDUls31VLZ/1ryJqZ7g5JEAQ3cvVCPP5EjIB7KDEKPnSyrGDT3gkoFA6mjtpC0C9aqHkmmazEUqbkbsVi07Bl3zisNs+deNlHpbShVtl9tAOKTFx404EvF/73odDH3NNI0ZY/sXX15ezZcDdtBj1JWZe6OyxBEASfIBJwwacYzQFsLJyAxaZmXOYulArbsXdyo5SYatLjKympSWfj3gleU1OtPNAX225XujkS59OqzWg1FloNEe4Oxa06WrbTXPs1smwn/4SnKZj5AkqVd/x9CsLxEIvxHFlfFxRX3PyN/z1jLyJGwYfHYtOyff8YNGoLGQkV7g7niIIDOslO3k95fRLl9al402irzd5bvaZS+t4CNX1XQmXvn9M7bLLsQKUOBCAwNJvQqAlIfniJWBAEwVVEAi74pB5zALUtcUSHNbs7lCNKjKrDbNFSUp3h7lCGzO5QIssc0ifaF5itGsxWNVGhre4OxW0qi16hcOO9qNTBJKRf4O5wBEHwFJLkupufEZMwBZ9ltWpQeewS4jLRYc00tMV4aR9tCZtd6ZMJuCwrqGmOJzm6hopG/1zdsbO9ELUmnCnz/g/JDy8NC4JwBK4qF/HD9xn/e8ZeRpShDJ/NoUSlsqGQPC8Jj4toRKexUN/ivV1E7A4VSh8sQQGoa45DrbL3L8TjT7oN++nq2Ifd1o0sO9wdjiAIgk8SCbjgs1o6IpCQmZS73aMmY4YGdpCbUkxTeyQGo/cuce6rI+AAJosWAI3a4uZIRl7Z7r9jt3ahDYhDoRAXSQVB+JmM5LKbvxEJuBcQo+DD020KZEtxAWFBBuIiGt0dTr+MhHLMFi27yka5O5TjYrOrfHISJvQuMgRg8pKuNM4UEjkOgODwfDdHIgiC4LtEAi74tLbOcJraI8hMLEOt8ozRzJDAThraYrDZ1e4O5bjY7EpUHnRlwZn6EvCeAyPh/sJq6aC+/BOCwkaTPuYWd4cjCIKHEW0Incf/nrGXEqPgw7enIheVwk5iVJ27Q6HvAp7D4f2X22x2FSqVr46A92B3SJit/pOAF29/gg1fnYPd1s2oSQ+hUgW4OyRBEASfJRJwwedZrFqMZj3hwe2A+5o767U9jM/ahVplx2AMdlsczmL34RpwvcaEyazDm3qzHy+VOgiA4PAxaPXeOzlYEAQXknBRG0J3P7GRJxJwwS/sr0knKrSN+IgGt8UQH1lPeHA7+6oyaesMd1sczmKzq/pXxPQ1vaP7vvnl4nCMneUkpF9AUNhoug37kWXf/P8qCIMlVsMUXE1Mcfci3396onhTGKamjijqWmIYk74XgzGIblPQiMfgcPR+361qTBjxc7uCza5C7aMj4EazHq3a6lHdc1xFlmW2rrmy//cx0/6CJCndF5AgCB5LRuGStSu8cz2M4+N/z1jwW3srs5EkCNIb3XL+5o5IVEo7YcG+0Vva5vDdEhTjge4nwfpuN0fiehZTEwDhMSeQf8LThEVPdnNEgiB4KlmSXHbzN2IE3MuIUfDhs9lVmC0awoPbaWgb+RrXpOhaAGQZdBrToPaRh1myrlH37qhVm5AOFNdZrBqnjjLYbCqUSge9dfVDefOU0aotuLMe/3AOfv+3WNUYTXrS4iuAVLfFNBIcjt7uQPFp5xEaNcHN0QiCIPgHkYALfkOvMaHVWOjqCXTD2WW0ajMAk3N3uPxsCoUCKGBG/kYcjt7VDHeUjqKhNc5p57A5essUVErbkFoqpsdXkpVY5rQ4XMnimwP8AxhadgASXR37CY+Z5u5wBEHwYK5qGeiPbQhFAu6FxCj48Djk3iFOq80d/bclCityqWrsGpHZ3moVFBTAtpJ8rDbISy0iIrjduQm4vfftY6gJeGhgB+1dIZTWDnZkeeQuTQ4Yk5dl8tLLDnrENy+RNlZ/CchUFr1EcvYid4cjCILg8eQDl6el4yidEQm44DfCgnprr1s7w9xyfotNQ2tnxIicq7cEpYO2znBUSiN6rZkWg3PP/XMCPrSOGbIsYXcoaDFEOjUeV9hfIzOd3r+dxjbv7VyjVlmw2VXI8sBRJqulA0Nr7xWZiNiZ7ghNEAQv4qpl471lKfrXX3+dJ598kuLiYgBycnK46667uOyyy4Z8LJGAeykxCj50oUEGzBYNVpvG3aGMqNTYaswWDU3tUU49rt3+cwnKULQYIhiVUkx4cJvHt2Ps/dJiIDWu0isTcElykJVYRmpsFQ5ZQashnMa2KJo6IrHaNMhyXw0/BIXlujdYQRAED/bUU09x//33c8sttzBz5kxkWWbdunXccMMNNDc3c8cddwzpeCIBF/yCQmEnIbKBmuZ4d4cyotQqC/GR9ZTWph0y+nm8jjUC7nBYUSgOLU2paY4nJryJidk72FOeS50Ty2Kcr3dUJiK4g9GpRXQag7DZVQduyoN+7v3d08pUkqJqSYuroqwuGatNTXRYC3lpRciA0RSAza4iIeBcPvnkY7Q6sfiOIBxs1sI1YhXqX/DnGvDnnnuO5cuXc/nll/ffd8455zBmzBiWLl0qEnBBOJzsxFKUCrvP9OAerABtD0qF7PTyE+BAwslhe2WX7XmB+opPGTPtSUIi8gc8JssKthYXMDp1H/kZe1GpbFQ1Jjk9Pmfq6gkkNryJhKg6FEfIsWWZXyTlP/9styuxDkjWD5/A936pcV4SHxLYSXtXCCU1mQBUNKSgUZmJDmshSN+NUmnHIYWh1ccRHjfbaecVBEHwNXV1dcyYMeOQ+2fMmEFdXd2QjycScC8mylAGJ0jfSXJMDfuqMjFZ9O4OZ0R19QTikHtrmDuNwU49tt2hRJYPHQG3WgzUln0AsoPCjYuZdPLbqNQDzy3LCvaU56JRWUiKrj1sAu6MSS7OsnHvRCxWCZBRKByolTZU/Tf7QT8f+rtWbUGlG/i4QnHkFow/J+YHJei23t+tdhV2hwqbbWDybv1FMt+XxMuyhEJyDDi+xaalpvnnL6JdHUrMPa/R2bqdiNhDP1wEQRD6uKpntzf0Ac/KyuL999/n3nvvHXD/e++9R3Z29pCPJxJwwedlxFdgNOupakp0dygjzu5Q0dYZRnpcJYbuYDq6Q514dAmbXXnIku3Ntd8gITFuzstsW3sdzXVriEs567D7a9RWOo0/r0ra2b6X5tpVKJV66io+JjJuDlkFv3dizMdLwuFQYnYoMVu1wzxGbxJ/uIRdfYSEXqO2EKAbuP3RkvidpaOpb42l0xhEfGQDkuQ4YglSYEgWKk0oXe1FIgEXBOGo/HkS5kMPPcRFF13Ed999x8yZM5Ekie+//55vvvmG999/f8jHEwm4lxOj4MemUVvp6A5xeg20t9hdNpqxGbuZmLODDYUT6TY5rw+6za4+ZBJme9MmgsPzexM7VSA2i+GI+2vVFlo6fp7cWFPyNi313/X/3lD5KWmjrkOl+eUIup3m2tU0Vn+Bw2FFrQnDam5DpQ4mMn420QmnICk89e2tN4m3OJRYjieJlxwDE3hVb4I+LnMP8ZEN1LfG9n+oyfKRP9wkSSIoNIcuQ/EwYxEEQfB9F1xwAT/99BNPP/00H3/8MbIsk5eXx4YNG5gwYeiLmHnqJ5QgOImMRm2h2xTg7kDcxmzVsrW4gGmjN5OTXMLW4nHH3EeWZYyd+2lr/AmH3UJIxFhCoyYi/WKijNWuQn1QAi7LdgytO4hPvwAASVLR3rSR6MR5aPXRA/YNDWxDozL392XvaNlBe/NmIuPmYLUasJpbsZhaUKj02GxGLKYmLD1NdHXso7Hqf/R0V6HRRfcvpd6nteF7Wuq+Y/SUPw/r9fIOEg5ZicWmPGSxoKIqM9mJpYBMVGjrgb/9o48uBYZk0VTztcuiFQTBN/jzJEyASZMm8eabbzrlWCIBF3xaoM5IoK6Hososd4fiMrLDRunuZ2mpX4dGF0lgSCZJaacCmf3b2B0qyutTyEsrQqfpGVAL39VRjLGzDI0uElN3LZ3thXS0bMVsrEOh1KNUaqkq/nfvRL2YaTgcFuzWbuz2HtLDx9Pe0cGO759BpQ3DYevBZu0kPHoKACm5V1O662n2bLiLglkvolT2jvgaO8vQm/9DeXkUe8tjkBRVFG68B7vNOGAEHGDDV2djtxn7f1codYRHTyVnwhKKtz9+SAIOYLebnPkSe5X2rlAUCpk549ajVVvZVTrqmPsEheZQs/8drOZ21Now1wcpCILghRwOByUlJTQ2NvavMt1nzpw5QzqWSMB9gChDOTLlEBeJ8TYOh5V9W/9ES/1a4tPOw27roat9D4Wb1gBvH5jI2Dv6Wd8Ww6jUfUSHtfRPemxtWM/eTQ8gyweGUSUFgcEZhEdPJSJ2JqFRE5AkFV3te6ir+BRD6w6UqoD+21erq1Eq9eiDUrBa2lEotcSmLCQgJAeAuNSz0OiiKNx4D41V/yMu9Ry6OorY89MfsVn7SlPeAGSUqp9LYzTaKAJDs9EFJqDVRaPRRaPRRaHRRaHVx/S3N8yf/gztTRvo6tiHoXUnXR1FIDuwWTqoLf0PUYmnotH+XOIiy3Y6mrdiMtYTGT8btcaZNfGewdAdwtbisQf6rIfR3HHs/u+BIb0TiLoMxf1fngRBEH7Jn2vAf/zxRxYtWkRFRUV/k4A+kiRhtw8t3xhSAr58+XKWL19OeXk5AGPGjOGBBx7g9NNPB3ovWz/00EO89NJLtLW1MW3aNJ5//nnGjBnTf4yioiKuuuoqKioquO6663jggQf6H0tLS6OiooIffviBE044of/+22+/nW3btrF69eohPTlBMHQH023SEx3W4hUrLw5VdfGbtNavY9SkZUTGzQJAlh3UlbwEwNY1V6ALKiAsZiphURNp6wwnKbqW6sZYGqpXU7L9cSJiZ5A1/h7Mxnp0gQkolbpDzhMcPobmujUYO8tQaUK50xbAujPeAcDc08imby4csH1z7Wrsts4B95XueobyPctxOMwEhY0mMGQOHS3biIybTXD4GEIiClAo1CiUGiRJOajnr9aEEJ14KtGJpwJgt/XQ3ryZpuqvKC98kbLC5YRHTyMm6TTCoiez64c76D5Q66zWhvW/Zr6muSOS5o7B/73rAhNQqgLpbN0lEnBBcDORa3mmG264gcmTJ/P5558THx9/3B26hlR0k5SUxGOPPcamTZvYtGkTJ598Mueccw67d+8G4IknnuCpp57i73//Oxs3biQuLo558+bR2fnzB/HNN9/MZZddxieffMKnn37KunXrBpxDp9Nx9913H9eT8kdisYAjkTBZtKhVVncH4nQmYx3V+98mMfOiAYmkJClIHX0dABGxszC07aRo84P89OVCVnz6OnptN6mhb1G87c9ExM0mZ+IDqFQByLL9sMl3H5u199+xzdLBX6R2ZEfvqLksH/qtPzblDDiopi8ybg5JWZeQOupaciY+QP4JT5FV8AcmnfQmaaOvJzJuFmpNCEqVftDJ9+EoVXoi42YxavIypsz7PzLG3IrV0k7RlqX89OVZ/cm3JCn9vuPH4i+u6/9ZkhRExs+hsearA6tjCoIAuOXqsifnWjKK/jpwp96Glo66RXFxMY888gijR48mLCyM0NDQAbehGtII+MKFCwf8/uc//5nly5fz448/kpeXxzPPPMN9993H+eefD8C///1vYmNjefvtt7n++usBaG9vZ8KECRQUFJCQkEBHR8eAY15//fUsX76cFStWcMYZZwz5CQnCL9nsqiOu1ujNaks/QHZYCQo9dAnxvm/mxs7SAaUdpSWbePllAycgUzDzBYLCRoNsZ//Op6mv+ISI2FmMnvKnw54ve9zdZBXcRU9XJZKk7O8yoguIZ+ZZqw/ZPj3vRic8y+FTa0KJTzuP+LTzMLTupHzPcmRkutoLD0wW3UVoZIHTz2u3mzB119DTVQVARNwsFB7YkeXRBS8N+D0maQGNVf+js23PIYsnCYIwckSu5ZmmTZtGSUkJWVnOmVM27E8Fu93Of/7zH7q7u5k+fTplZWXU19czf/78/m20Wi0nnngi69ev7/+jWLZsGfPmzaOnp4ezzjqL0047bcBx09LSuOGGG1i8eDELFixAofD8b0WeQtSCH0qpsBES0ElbZ5i7Q3G6+LTz6DaUsHfzA4RGTSQ64VRUmmCMneXYTGVwxe9w2C1odD/XAEfGn0j6hKXsqPwcQ8Un6Jo2ULXvtf7Hb2sq46uDzmHuaaS27P9IHXUNCoUaSVIQEJw2Ys/RWUIixlIw6wUcDhs/rOgtV7GYm512fLvNSNnu52lr2nDIpFBdQALJ2ZcTnXiqB7dGhJCIfJTqIDqat4gEXBA8hKflWv5cA37rrbfy+9//nvr6esaOHYtarR7weEHB0AZ0hvxpsHPnTqZPn47JZCIoKIiPPvqIvLw81q9fD0BsbOyA7WNjY6moqOj//YwzzqCpqQmDwUB09MC2ZH2WLFnCq6++yltvvcVll1021BCxWq1YrSNbctB3vpE+7y9p1EdenMMb9MXvjOdh7ConM7kcrUZNVXOq1782v6QJT2TinKdprV9LfeXn1BT/HZBRqYMIj+qdVDdh9pPs/Olh9Ho9Vyii2Dt1CdUlr1Fb2rtoQFcLTNZHsEeyIss2vjjxL9y75laeOvVZALrbymir+y/YG8md+MCRQvEiSuYsXEHJjr9QVfgXJLmd+NRzj1jLN9i/x7J9r9DVtp7kjLPQB6WgC0xEF5CA1dxKdcnbVO/7G801H5CYeTFR8SceV5mN6yiITZiCuXu3U/+tOPPftD8Tr6NzDOd1dObn+mCP5am5Vu9KmK5oQ+j5CfgFF/S2173qqqv675MkCVmWXT8JEyA3N5dt27bR3t7O//3f/3HFFVewZs3Po66//CDrC+xgWq32iH8QANHR0fzhD3/ggQce4KKLLhpqiHz11VcEBLin7/PKlSvdct4+t13h1tM7zY2Ljrx4y+CFH7jBxAkWwOKEY3qicQduh7rpki645I7+3xfQDZx34HZ49hse5jb6LlfmAu8c+LnjCHt4o98duAEc+2/t2H+Plx24/VIEcMuBW5+uQcTnJlfcduAH5/+/ds6/aUG8js4xlNdxxYoVTjuv0Wg89kZ4R67lb8rKypx6vCEn4BqNpr/+ZfLkyWzcuJG//e1v/cX89fX1xMfH92/f2Nh4yDe1wbjzzjt54YUXeOGFF4a87/z58wkJCRnyfsfDarWycuVK5s2bd8hliZF22kXrjr2Rh9KoZW5cZGD52yFYrMP/Rrxv658xtO4AIDhiLLkTljgrxEHrncwmHfdM6eE4+HU0W6Cy6BUaq7/EbutGodCSP/NvBASmUFX8BnVlH/KuIrl/377R7z4/ftFbH3jCAud9CHmKtsaNlOx8EtlhI2f8fYRFTxrw+OH+Hh12C421K6kr/Q/mnsb+bWOSF5Ax5nccTVfHPir2vkJ3RxF5Ux8nKOzQ+n136jbsZ+f6W8mb+hghEc6pj3fWv2l/J15H5xjO6/jlezOddn6DYXCJv6fmWv5cgpKamurU4x13QaIsy5jNZtLT04mLi2PlypX9S3JaLBbWrFnD448/PuTjBgUFcf/997N06dJDJiQci1qtdlsS7M5z9/GFN2eLVRr282hr/Ima8r5V/RSkF/wai7X3MhGyHYdsR3ZYsdt6MJsakR1WNNpIdAHxx12jK8t2Gqu/oqVuDR3NW4lNXUjGmFuOvaOLWKwSVptEfOa1xGVcRU9XJWpNOAqlntamfZQX/Qe7zchjZz7H4i+u49EFL7H40+v6J+g5HDZ6enr6j+VrAsOn0mXordnevfExJpz0OirVoVfPjMYuLFYFTbXfUF38FhZTE1EJJ5GQdRNafRySpEAXmHjM10gTkEvmuIfZ9eOdbFu/mHGz/oFWP/QPTVdR6TKw2tQ01m5BF3zsFVOH4nj+TQs/E6+jcwzldXTmZ/pwj+WJuZa/2rNnD5WVlVgsA6+qn3322UM6zpCyjXvvvZfTTz+d5ORkOjs7effdd1m9ejVffPEFkiRx++2388gjj5CdnU12djaPPPIIAQEBLFq0aEhB9bnuuut4+umneeedd5g2bdqwjuGPfGEypsNho2zPPzH3NBESPoaEjF8NfueD6tOCw0dTuvMZjJ2lOBxW4Mh1fyp1MGHRUwiPOYHw6KlDXhHQZu2mcOM9GFp3EhIxjqjEk6kr+wBzTwNqdQgafTRJmYtQKDVDOq6zSJKSgOB0akv/Q/nel5AdVpSqQOLTe1/bvqT74O4YdeUfARARN3vkAx5hFnMzRkMpIRH5OOxmrJZ2Opp2ApPZ9M1F/V9EImJnkTrtSQKChzcaolBqGT35z2xfdyN7Nixm7IznUKkDj73jCJAkBaFRE2mpX0tyzpVuuXojCP7Ok3Ot3hpwF4yAe8F7TWlpKeeddx47d+7sr/2Gn8uBXFoD3tDQwGWXXUZdXR2hoaEUFBTwxRdfMG/ePAD++Mc/0tPTw0033dTfHP6rr74iODh4SEH1UavVPPzww8P+oxK8i8NuZsdPt8MVj7Jnwz00129DF5hMW8N64tPPO+rENYfDRkfzZkzGOqzmtv77u9qLiIibTXTiKSiUOiSFCklSHVjwRYtWH4ukUGExNWFo2UFb448Ub3sEJAUxifNIyroMfVDSoOIv2fEkhtad5E//G6GR45BlGV1AAu1Nm7CYmmms+QqruZ3Msbcf5ys1PN2GUurKP6Sh8jMAxs54jqCwUf2rSvbp6w999+TrKd/zPACjJj00ssGOoPi086kr/xCA8sLlGLsqsFt767T1ej3wDgFB6fT07AEgODxv2Ml3H7U2jLwpj7Jj3c0UbXmIvCmPeEyHlLjUhez+8fcYWrYRGjXB3eEIgt8RuZZnuu2220hPT+frr78mIyODDRs20NLSwu9//3v+8pe/DPl4Q3rHf+WVV476uCRJLF26lKVLlw45EKB/1aeDXXzxxVx88cXDOp7gbSSMXeUAmHtqSc//HSpVEPu2PozF1IJWH3PIHj1d1TRUfU5j9RdYzW1IkhK1JpzAkGwi42eTkP5rlCr9Mc8cEJRCWNQkUnJ/i8XUQlPtKmr2v0tj9Uoi42YRGX8iIREFaLThR0yUNNrelQe7O/bRUPkpHS07CAzJIH3MLQSFZlNX8QmlO58mJGIs0YmnDP9lGgarxcCeDXdjMTWhUGgZNflhQiLG9pedAP0/P7rgJRZ/cR2jf/oTu4Fxs19CcsGsd0+RnHMFFnMr3Yb9aLSRhMdMp7V+be+y9gcYu3on36TkXk1ipnMmKwUEpzFq0kPs3vBHSnf/nYz82zxixDk0ciL6oDRqyz8UCbgg0LsYz0gudufJuZYsS8iyC0bAXXBMZ/vhhx9YtWoV0dHRKBQKFAoFs2bN4tFHH+V3v/sdW7duHdLxPGPIRXA6byxDUSg16IN6JwNOOultzBbY/n1vT9Pm2m8xdlWgD0ohNvkM2hp/pKHycwyt21Gqg4hJnE9M8ukEhmQed7L44OrFPLrgJeJTz6ahagWNVV+wb+vDfVGi1oah0Uai0UWi1kag0UZg7mmgs613lbKa0v9gMTUSGTcHY2cZ29deS+qo60nM/A2drTsp3v4YNlsX4VFT0AbE01i1AqulnaSsS44rblm2Y7caUWkOHQUxdVdjMTWRknMVyTmXHxjlfhkYuCLiwT9Xn/Y2Ux0W1Jqhr/DlTAd/SXAFtSaUUZOWDrivu2PfgAQ8JfcqIhLOc/qCOmHRk8nMv5P9O/+CPiiZhPQLnHr84ZAkiYT089m/8xlMxjp0AfHH3kkQBMHH2e12goKCAIiKiqK2tpbc3FxSU1MpKio6xt6HEgm44FECgjMAsNt6kB0qeroqgd7SgD4Vhf8AICRyPDkTlhARNxulUuu0GPqSPYVS27+S4jX/+y2lspk2bLRa7XwTOwqruRWjoZR28yY0ukjCY6YRFDYKlTqMkh2P09qwjsj4ufR0V9FtKEGSJLIK/ojDYaN059MAqNQh2Ky9s+ITMy8+ri8PtaX/obzwHwQEZxCfPBO44EDdu4bg8DzCY6ZTXfIWDtmGWXbw1Okv9yfcfaPeB1vy9W0uTXwHyx0xjJq8DACl1A1YSUj/lcsmvsWlnkVPdxVlu59HFxBPROwMl5xnKKIT51G+9yXqyj92+4qmgiB4ElctG+/5V1nz8/PZsWMHGRkZTJs2jSeeeAKNRsNLL71ERkbGkI/n+c9YGLaRvGTmLIkZFwJQvvdFFEoN4+e8gvpAaUf/NpmLmDj3TcZOf4boxFOdmnwfycunv8o0RRALFGEsUkSSVfB7Rk/5M+92O/jQFsa7XXYy8m8jJuk0ImKnMenkt0nJvYb2po3og1JJHXUt0DvKP2rSUqbO/4TRUx4lLvVsQiLGIUlKzD0NxxVjXzcNY2cptWUfALDpm99QsfdlHA4bORPvJz79Amr2v8NV4aHYrJ0Dyk/g0GT3l0m5v1EepiOKK6SNvo6I2BkUbVlGe/OWETnn0ShVemKTz6Sh6nNstsH1LRYEwff1tSF0xc3TLVmyBIfDAcCf/vQnKioqmD17NitWrODZZ589xt6HEiPggkcJCE4HOmiqXklE/DmU7HgSq7kFgPS8m4lLO/eQSYMj5XCjxEcanVUqdSRlXUxi5m+AQxdNUGtCiYidTkTsdGzWTravvZ7Cjfcydubzh22DNxhRCSeh1kZStGUp0Um9XUvOdmh5r+RNHA4r6Xk3kjb6OsJjprJnwz1sWHk+YVGTuD3vXCLiZqLWhA76+QnOJUlKcibcx95N97P7xz+QknsVSVmL3Fp7n5B+AXXlH1Jf/tFxl0cJgiB4u9NOO63/54yMDPbs2UNrayvh4eHDmr8jRsB9nLeNgstyXxsfmZ3rb6WrvZDI+LlMW7CChIxfuy35hiOPEh9tW0n6eSGeI40mN1SuwG7vwdhZRnXxm8c8tsNhxdC6g46WHXQb9h9Y8KdXaGTBgcmgve2R6k97mwVSKLWl71G+5x9Ul7yNVhfDxJPeJG30DdhtPZTseJINK89jy7eXcUV0An+e//wxYxCcT6nSkzftcZKyL6Wy6GUKN96L1eK+VQ+1+hhik8+kZv97YhRcEATAv0fAX3vttf52tH0iIiKGPXlejIALHsNm7aJs51/hyt7lsO22bgDS824a9qiwMw0l8f7lz0faX5ZlKoteweHobejvsJsPu6Rwn6aabyjd9Qw2a2f/fYEhWWSNu5ug0GzMPQ10G4pJH93bqePOr39HhSKMH6MLaK77FqvFQFXxGySkX0BCxq9ISL8Ai7mV1vrvMXaWUV/xX6L+92scimgeP/2fx35RBKeSJCWpuVcREj6GfVv/zPa115E7aSnBYaPcEk9S1iIaqj6nruxDkrMvdUsMgiAInmDx4sX87ne/49e//jVXX301M2Yc33wdkYALHqGro5iizUtR8HNiqVKHMHHu60NeEMddjpRwHy0pB9gtxfLX5LG01n9PXfn/0db4A5FxcwiNnkRI+Jj+OuRuw35Ktj9OeMwJJGYtQqnSYzY2ULbn75Tu+hsFM/9OZ1tvr+qQ8N5lxJ869VksVonRB851+/+u4TVHE1+Uf0RD1QpGTXqIkIixxKX2ruClC0jk0z1/Z7O9m+tXXMJXZ7zl8i4kwqHCY6YxbvZLFG1eys71t5Ix5lZiUxaOeJvCvlHw2tL3iU87z2MWDBIEwT38eSn66upqPv/8c1577TVOOukk0tPT+e1vf8sVV1xBXFzckI8nEnA/4OktCR0OK7t+vAO7tevAwieQOupaIhLOOeriO57mcEnq4ZLXX9aSj5H0/KuuBJscy73T7idpw8N8W/0lNaXvIklKgkJzCQjJpK3xB/RByWRPuK9/4mlAUCoOx7Xs3XQ/1fvfpan6S4JCR6HVRwMdh8TzzIHOJxfJNm4O0FO05WEmzv13f6/0hIxfodIEU7ztUbbJRr+fhOlOuoA4xs54lrI9L7B/51MYWneSOfbOQfW1d6akrEt6R8HLPyQ5+7IRPbcgCIKnUCqVnH322Zx99tk0Njby5ptv8tprr3H//fezYMECrr76ahYuXIhCMbjqbpGAC25nNJRit3YRETsbydE74TI+7TyXtX0bSYdLvvv++8vkViVJPLH5n6CMo2vei/R0VdLRug1DyzYMrTsIjZxASu5Vh3R9iYidQWBoDhWF/0AbEE/OhPsHFdPT/7uSG+1N7N/5NLHJp9Nct4bg8DyKtz2ORhvF/il/4lE3lT4IvRRKDZljbyckIp+SHX+h21BM7qRlBASljFgMWn00cSlnHRgFP1+MgguCH/PnEfCDxcTEMHPmTIqKiti3bx87d+7kyiuvJCwsjFdffZW5c+ce8xgiAfcTnjoKbuyqpHDTEnQBCeRMWHxgBPzQkVtfdLSyDgkICE4lIDiV+NRzjnocSVIydsaz9HRWEBiafaBzhnzM8796+mtE73mB2tL3aar5GnBQX/ExQWGjGTv9GRRHaO84lMmognNEJ55KYEgWezc/wK4fbmP8nH+h0YaP2PkTsy6hvvIzqor/TXreTSN2XkEQBE/S0NDAG2+8wauvvkppaSnnnnsun332Gaeeeio9PT0sWbKEK664goqKimMeS3RBEdxClmVa6taya/1tKFWB5M94dsR6LvsipVJHUFjukNvWWc3tAMQkL2DSye8SnXgqWQV3HTP5/uXPR7tPcI6A4DTyT3gaZJmS7Y8jy8f+kuUsWl0UKblXU1v6HwytO0bsvILgKTxxAMsd+paid8XN0y1cuJDk5GRee+01rr32WmpqanjnnXc49dRTAdDr9fz+97+nqqpqUMcTI+DCiOs2lFC2+3k6WrYSHnMC2ePu8ZqJlr4mbfT1JGb+hsCQ3lW8ciYsOer2xxr1FqPirqXRRZI17m4KNy6mvuJj4tPOG7FzJ2b8mtb67yne9hjj57wsvjALgh/y5xKUmJgY1qxZw/Tp04+4TXx8PGVlZYM6nhgB9yPu7gluMbdRsuMvbPvuWizmFvKmPk7e1MdE8u1GGl1kf/IteIeI2OlExs2hsfqrET2vJCnJHn8PFnMr5YUvjui5BUEQ3GXVqlXk5eXx9NNPH5J8d3R0MGbMGNauXQv0rv2Rmpo6qOOKEXDB5Rx2C3XlH1JV/AZIEuljbiEu9RwUCvHnJwjDodFFY+wqH/Hzmo31hEaOp77iEyLiZhEePWXEYxAEwX38cQT8mWee4dprryUkJOSQx0JDQ7n++ut56qmnmD179pCOK0bA/cxwRsENbbvZ/v2NVBa9OqT9HHYzVcVvsumbiyjf+xLRifOYdNJbJKRfIJJvQTgOVks7as3ITcLsU7hpCW2NPwJQsv2JAQtCCYIg+KLt27ezYMGCIz4+f/58Nm/ePOTjiixIOKbujn10tRfS1V6ISh1EZMJJByaB2VFrwgkKyyUoJBu7vQeFQkNQWC6t9euoKn4dc08DsSkLSUi/AH1QsrufijAEYgEez+Wwm1EqdSN+3oDgdLraC4lLPYemmq8p2/13sscvHvE4BGGkubuE01P44wh4Q0MDarX6iI+rVCqampqGfFyRgAvHFJN8OlXFb2I1t1C253nK9rwAyIRGTcTc00Br/fc4HOZD9guLnsroqY8SEDS4eijBs4jk23M5HGaUypFdkAcgKDSHrvZC6is+QReQQGP1l0TEzSYybtaIxyIIgjASEhMT2blzJ1lZWYd9fMeOHcTHxw/5uKIExQ8N9Zu8UqkjOftyQCJ19A0kZv6GiSe9Sf4JTzFq0kNwUOs7pSqQjDG/Y/Ip7zNm2hMi+RYEF3DYLSiUmhE/b0zy6Yfc11uK0j3isQiCMPJkXNSG0INHwM844wweeOABTCbTIY/19PTw4IMPctZZZw35uGIEXBiU2JQzqCl9l+riNwiNHI9aEwbApm8uRJbtAORNfYKw6ClIkuf+QxIEX2C3daFQJI34eYNCc/p/NhlrAbBZDdRXfkpS5m9GPB5BGAmi/MS/LVmyhA8//JCcnBxuueUWcnNzkSSJwsJCnn/+eex2O/fdd9+QjysScD81mJUxywtfxGbtImPMLSiUWtLzbmLvpvtpbVhHV0cxYVETyJ/+N2zWTsJjpg15ERhBEIbO3NNIt2E/Cem/HvFzS5ICXUAiJmPNgPvDoiaOeCyCIIw8BxIOF4xWu+KYzhIbG8v69eu58cYbWbx4cf8iaJIkcdppp/HCCy8QGxs75OOKBFw4LFmWaaj8HJvVQGfbTkDC2FmOUhWI3dZNc+0qwqImEBKR7+5QBcGvNNWuQqHQEBE3tJZXzhKdNJ+qfT93RAqLnjJgZFwQfIkY/RYAUlNTWbFiBW1tbZSUlCDLMtnZ2YSHD78blRiy9GNHe2OxmBqxWQ0k51yJWhOBw2EDZOy23lrPkIixIxSlIAgHa675mvDYGajUgW45f3zqOQN+1+rj3BKHILiaSL4P1dcFxRU3bxAeHs6UKVOYOnXqcSXfIEbAhV/o6iimtWE9gcHpANit3XS0bOl/PC71XJKyLkarH/rlFkEQjo+xq4JuQwnJOVe4LQa1NozUUddTsbd3Ncy+L+WCIPi+vkmTrjiuvxEJuEBb40+0N23CYm6mufbbAY/Vlv0HgODwfHImLEEXIEa7BMFdOpq3IEkqwty8AmVsypn9CXh70ya3xiIIriBGvwVXEwm4n1v90UzU6rn9v8emLKSt8QcspmYAFAot+TP+RnDYKDdFKAhCn46WHQSFjXLLIjwHU2tCiIw/kZa6NdisBiymFjS6SLfGJAiC68m4ZtEc2elH9HyiBtzPKZXKAUta936Y9ibfE096k+lnfCmSb0HwALIsY2jdTmjkOHeHAkBS1qX9P9eVf+TGSARBELyPSMD9nCRJbNzwLUGhuajUobTU9bYmTMm9Cl1AopujEwShj6m7Gqu5lZCIAppqvqZk+5MYWne5LZ6g0Gx0gb29yKtL3sThsLotFkFwJlF+cmQuWYTHRXXlnk4k4AIrV66kq6MIh9z7ARqfdh7J2ZeLBXUEwYN0tG4HFASH51O661kaqj5n1w+30d68hZb675Flx4jHlDbq+v6fa/a/O+LnFwRB8FYiAfczDQ0NnHvuuYwdO5aFCxcyffp07rrrLpKyLkUhaVAodaSPucXdYQqC8AuGlh0EhmahUgeSNro38ZVlO7t/vJO9m5ZQUfiSU86z+IvrBr1tRNwMJIUagM7W3f0LVAiCtxKj30fn720InUkk4H7mgw8+4JNPPkGhUCDLMgkJCTz99NNUl7yJQqnhhAX/Q5KU7g5TEIRfcNhNOOxmZNlObMqZqNTBRCeeStroGwgITjswQn78Hl0w+ERekpSkjroWJAVtTT9SsfefTolBEATB14kuKH7mmmuu4f3332fPnj18+OGHJCUlodP1dlQICs0RZSeC4KESMy9mx7qb2L72egJDc7BZOwmLngrIGDsryBx7p1viik0+ncqif+Gwm6jZ/zapo64V7yOCVxKj38cm+oA7jxgB9zNarZaPPvqIiIgIzjzzTO69914AAgICGDVpqXuDEwThiILDR5M2+nq6DSU01awkInYWkqSkeNvjxKacSWzKmW6JS6UOJjb59P7fu9r3uiUOQRBcTwYcLrj5Y/GaSMD9UEREBLfeeivFxcU89dRTAGzZsoV1n5/i5sgEQTiaxMzfMO20T5lxxkrS8m5g/86/Ehk/m8yxdyJJ7ns7j087v//n6v1vuy0OQRAEbyEScD/1/vvv43D0dk147LHHyM3NdXNEgiAMhkodjMXcRtHmh1Brwskq+KNbk28AfVAyoVGTAGitX4vVYnBrPIIwVKL8ZHBEG0LnEQm4D+vp6Tns/eXl5axdu7b/97vuuqv/Z/EmJAiera78YzauPI9uw35yJy1FpQ50d0gAJGVe3P9zQ+XnboxEEATB84kE3IcFBAQQHBxMW1vbgPvfeOON/p8/+OADFArxZyAI3kKrjwUgKCyXwJAsN0fzs9CoSWgD4gGo2Psismx3c0SCMDhi4GnwRBtC5xGZl4/r6uoiJyeHv/71rxiNRurr63nyySf7Hz/nnHPcGJ0gCEMVETudUZP/RFd7Ic21q5x+/KH0AT+YJEkkZS7q/72tcYOzQhIEQfA5IgH3YatW9X44d3d384c//IG0tDTy8/OxWCwALFu2DJXq0E6UYjRAEDxbZNwswmNO6G3/57A59dhD6QP+S9FJ81Ao9QDs3fyAs0ISBMFDiBpw5xEJuA+bO3cus2fP7q8Fb2pqwmKxYDabAbjxxhvdGZ4gCMchddQ1mIw1NFZ94e5Q+imVOuLTzgNAdlgxm5rdHJEgHJ0YcBLcRSTgPkySJL7++mvWr1/PY489xowZM+js7OTKK6/kvvvuIyoq6oj7ijclQfBsgSFZRCWcTFXxazjsZneH0y8+7Vw4UM9paHHO6pyCIHgGUQPuPCIB93EajYbp06dz99138+c//xmA1157rf9nQRC8V0rOb7GYW6mr+O+InfNYNeJafQwpuVeNUDSCIIwkh+y621C98MILpKeno9PpmDRp0oDubkezbt06VCoV48ePH/pJnUgk4H7kjTfeICQkxN1hCILgJPqgZGKTTqe65E1s1i6Xn68v+T5WEp6UdSmR8SdSsuMvWM0dLo9LEIZDXOn1Xu+99x6333479913H1u3bmX27NmcfvrpVFZWHnW/jo4OLr/8ck45xf0LD4oE3E+UlJTw6quv8vvf/37Q+4g3J0HwfMk5VyA7rJTufm7A/bIs43BYkWWHU84zlO4okiSROfZOkO001nzplPMLguB+ri5BMRgMA259c9Z+6amnnuLqq6/mmmuuYfTo0TzzzDMkJyezfPnyo8Z//fXXs2jRIqZPn+7012aoRALuJz788EMUCgWLFi069saCIHgNrT6GjPzbaKr+kqaarwGoLnmHzd8u4ocV81j/+cnHNTq++IvrBiTfg+2SotaEEh47k7bGn4Z9bmeSZQdNNd9QtOUhSnb8hdrSD2hv3oqxqwKrpd3d4QmCACQnJxMaGtp/e/TRRw/ZxmKxsHnzZubPnz/g/vnz57N+/fojHvvVV19l//79PPjgg06PezgO7UEn+KSTTz4Zu93Oxo0b+++zWq2o1eqj7vf9pycya+EaV4cnCMJxiE6cT3vTZoq3PYbDbqFm/9todL2TrDW6aJSqgGEf+3AJ92CT8ODwPJoq3hz2uZ2ppvR9Kgr/ccTHw6ImH1hZNGgEoxIE7+KqloF9x6yqqhpQKqvVag/Ztrm5GbvdTmxs7ID7Y2Njqa+vP+zxi4uLueeee1i7du1h2y+7gxgB9xOTJk0iPj5+QALe1NTkxogEQXAWSZLIGvdHohJOomTHE9isneiD0gDIKvgDkuS8t/qhlKIEh+XicFicdu5fkmU7hrbdyPKxZ3DFJi0gPe8WssffR+bYO4lLPRuV+ucP+vbmTWxedTFWi6hZ9wdfvjfT3SEIhxESEjLgdrgEvI8kDfwiIMvyIfcB2O12Fi1axEMPPUROTo7TYx4uz/gaILicJEmccMIJbN68uf++wsJCEhIS3BiVIAjOolCoyJlwH0nZl2I0lKHWhtNS963TRnQXf3Edjy54aUgL9fR9CXCVurIPKdvzPKGRE8kYexsBQakA2O1mZIcFhVKPJCkxG+vo7NhLUNgoQiLy+/fPHHtnbxLfuotdP9yGzdrJ7h//wOgpf0Krjz3SaQXBb8ly780Vxx2sqKgolErlIaPdjY2Nh4yKA3R2drJp0ya2bt3KLbfcAoDD4UCWZVQqFV999RUnn3zyccU/HCIB9yOnn346N9xwA2eccQYrVqzg888/H9RMYFGGIgjeIyAoFUPLdlobemshlapAALoNpXS0bCUu9RwUiqG/9Q9nhUylSj/kfQ6ns62Q5rpvScpchFob1n9/Y81KtPo4TMYatq6+Am1APPrAJAytu3DYexcgQ1LAgYmoCqWeafM/QaHU9B9DkpSERo6jYOZydqy7kW5DMZtXXUL6mJv7FxUSBMFzaDQaJk2axMqVKznvvJ//ja5cuZJzzjnnkO1DQkLYuXPngPteeOEFVq1axQcffEB6errLYz4cUYLiRy688EIcDgdxcXFAb1tCQRB8T2P1VzTVfAVAT3cNTTXfsO27qyjb/Rw2a+eIxSFJKpzxMdPRspXa0vfZsPJcmmq+QZZl2pu3YuquJiJuJhNOfI1Rkx8mPHoaxs6yn5Nv6E++ARz2Hux242HPERw+msyCP/TuItso3/MP2ho3DKq8RRD8hQPJZbehuPPOO3n55Zf517/+RWFhIXfccQeVlZXccMMNACxevJjLL78cAIVCQX5+/oBbTEwMOp2O/Px8AgMDnf46DYYYAfcjoaGhJCQk9P+xNTc3s2/fvkHVRIlRcEHwHomZv2HvpiUA1Ja+j6G1d0XK9Lxb0GjDRywOSZJQKI9cwzlYiZkXY7N2UrP/HfZtfZj9O/+K3WYkNHIC8ann0ly3mrbGn+ho3ozN2olSFUhgSBb6oBTU2jDUmt6bPjAJtSbsiOeJSzmL7o4S6is+xuEws2fDHwmJKCB7/GJ0AfHH/TwEwdu5ehLmYF100UW0tLSwbNky6urqyM/PZ8WKFaSm9pah1dXVHbMnuLuJBNzP5OTk0NDQQEpKCpWVlXz22Wfceeed7g5LEAQnsZha+pNvoD/5DgobTXz6BSMai8NhQ3bCJExJkkgbfT1BobkUbVmK3WYkIDgNSaFm65rfIst2gsJGEZ92PmHRUwgOG4U0jDIbgIz82wiJyKdkx18ICE7H3NPAtrXXMumkN4+avAuCMLJuuukmbrrppsM+9tprrx1136VLl7J06VLnBzUEogTFz+Tl5bFp06b+OqlPP/3UzREJguBMCqVuwO8RcXMBsNuMWEwj2/nIZKxFlu1OO15UwlwKZv2DkMjx2KxdOOxm0vJuYsqpHzBu1nJScn9LSET+sJNv6E32oxNPJSxqMsgyedOewG7torujxGnPQxC8Vd8kTFfc/I1IwP3M+eefT2lpKUlJSQCsXr2a9vb2Qe0rVsYUBM+nUgeSO+mh/t+DQjPIHn8fNksHFXtfHtFYeroqnH7M4LBRjJ3+DFNO/YCxM/5GQvr5aHSRTj9Pt6GY4Igx6AISQVLQY6xx+jkEQfBfogTFz8ydO5cxY8bw8ccf99/31VdfceGFF7ovKEEQnCoq/kQiz/wWh72nfxGezrZd/eUoI6Wnq7K/C4u3kSQlyA5k2YZaHYKpWyTggnDwsvHOPq6/ESPgfkapVPL000/zww8/MH78eAD+97//uTcoQRCcTpKkAStgBoZkY+yqxG43j1gMxq5K9IHJI3Y+Z+npqsJkrEWtjWD72huw24yERU9xd1iCIPgQkYD7oXnz5rFw4UKqq6uBY09WOJgoQxEE7xQYmgWyA6OhdMTO2dNZgT4oacTO5yw2WzcAlUWvADBu9ouEiwRcEHDIrrv5G5GA+6m//vWvdHT8vOTyYOvABUHwToHB6SAp6OrYNyLnk2WZnu5KdIEpI3I+Z3LYfu4jHp92DgHB7lmoQxAE3yUScD+VnZ3NrbfeilKpBMBmsw16XzEKLgjeR6HUEhY1idqy/+CwH39rwGOxWQ3YbUZ0Xrake1dHMSU7n0J9oF+6aD3oW758b6a7Q/BuB/qAO/uGC3qLezqRgPux+++/n7S0NACefvpp9wYjCILLpefdhMlYS23Zf1x+Lrutd8VJldp7JmE2VK5g+9prsVs7ySq4CwCVSMAFoZ9oQ+g8IgH3Y2FhYf0rYYoSFEHwfQHB6cSnnU/VvtepK//YqT26f6kvAfemLijygWXrZWTsB8pQNNowN0YkCIKvEgm4n1MoFHR0dBAbO7TLxKIMRRC8U2ru1UQnnkrprmfYse4WWuq/7082nanbsB8AhUrv9GO7SlzqWUw59f9QKnXs2/owKnUwWn2cu8MSBI/hQHLZzd+IPuB+zm6309TUNOQEXBAE76RU6ckadxcxyadRuus59m5agqRQExo5gcTMiwiLmnTc57DbzRRve6T3fMqAY2ztWTS6SPKmPs7+nU+TmHkxSi/6AiEIgvcQCbifa21txeFwiARcEPxMSEQB4+f8k57uatoafqChagUl259g8invHfexm2pWAqALSECtDQWMx33MkRQQnMbYGX9zdxiC4HFcVa8tasAFv7N3716A/qXph0KUoQiC99MHJpGQ8WuSsi7F3NOAsavyuI7nsJup2f8uEbEzmXTy2ygUaidFKgiC4DtEAu7n3nnnHZKSkpg4caK7QxEEwY3CY05AF5BI0ealx1UTXrnvNcw9DaSOutaJ0QmC4Alc0YKwvxWhnxEJuB+zWCy8//77LFq0CIVieH8KYhRcEHyDSh3IqMkPYzLWUrLjSeRhXBM29zRQs/89UnKuJCA4zflBehBZlrHZvKu0RhAEzyEScD/20ksv0drayhVXXOHuUARB8ACBIRlkFdxFc+0q2ps2DHn/3s4nDqIT5zs/OA9TtvvvbPr6V3QbSt0diiCMGLEUvfOIBNxPtbW1sXTpUn7729+Sl5fn7nAEQfAQUQknExyeT8Xel/v7Yg+WqbsGhUKLRhfpoug8g91upr7iE+w2I7t/+gOdbYXuDkkQRoRYiMd5RALup/70pz9hMpn405/+dNzHEmUoguA7JEkiddS1dBuKaalbM6R9jV2V6ALjkSTf/mhpqPwMWbYBYDW3smPdjZTv+QcOh9XNkQmC4C18+11SOKzi4mKee+457rnnHuLj490djiAIHiY0chxh0VMp2fEkjVVf9K9qeTR2u5mWujWoNOFYLe2uD9KNwqInkzb6BkZPeYTknN8CUFP6Lj+smMfun+6iq6PYzREKR3LaRevcHYJXk5FcdvM3IgH3Q3fffTdxcXH8/ve/d3cogiB4IFm209NVid1mpHj7Y2z46tyjJpU2axd7N96HzdqJoWUrO9f/bgSjHXkBQakkZv6GiNgZpORcwbjZ/6Tv47S9aSPb115Ldcnb7g1SEASPNqQE/NFHH2XKlCkEBwcTExPDueeeS1FR0YBtrrzySiRJGnA74YQTBmxTVFTEzJkzSUpKYtmyZQMeS0tLQ5IkfvzxxwH333777cydO3co4QqHsXbtWj766CMeffRR9HrnrfAmylAEwXcYWndi7qknMm4OAA6HheqSNwdsY7MZqSv/iMKN97F97fV0thcCvYWc2eMWj3TIh3DYzRi7KofVzWWogkKzmXnWKqbM+5CM/NsBqNj7EuWFL47I+QXf4sm5lgMXTcI8rlfMOw0pAV+zZg0333wzP/74IytXrsRmszF//ny6u7sHbLdgwQLq6ur6bytWrBjw+M0338xll13GJ598wqeffsq6dQMvCel0Ou6+++5hPiXhaJ5//nlGjx7NxRdf7O5QBEHwUCp1CCARGJLF6CmPAmDuaWTXD3dQvP0JSnb8lU1f/4rS3c9ht/WgDYhHodQBEBY9leDw0W6Mvte2tdeydfXlbPvutzRU/Q9Ztrv8nBptBPFp55I1rvfzq2b/OxRuuu+4+qoL/kfkWv5hSEvRf/HFFwN+f/XVV4mJiWHz5s3MmTOn/36tVktcXNwRj9Pe3s6ECRMoKCggISGBjo6OAY9ff/31LF++nBUrVnDGGWcMJUThKFpbW/noo4945JFHht33+2i+//REZi0c2qQtQRA8T2BIBvFp51G571/991nNbWhDc+g2lGCzdhKfdj5xqWej1oSy8etfYbN2ApAzYYm7wh6g90sEaAMSKNn+OO1Nm8iZcC+SpDzs9jZLJyZjLXabEY0uGn3Q0FcH7hObfDoabQR7NtxNW8N6qopfJ2309cM+nuBfPDnXEkvRO8+QEvBf6vufGRERMeD+1atXExMTQ1hYGCeeeCJ//vOfiYmJ6X982bJlzJs3j56eHs466yxOO+20AfunpaVxww03sHjxYhYsWOCSZNEfvfPOO9jtdi699FJ3hyIIgodLH3MLUQknYbN2owuIRx+UgiQdOlGq8EDtN8C42S+h1oSMdKiHFRk/h66OInInLKG1YT37tv6JuJSzCI2aMGA7u91E0eaHaGv8YcD9ydlXkJL722GfPzxmGlnj7qZk++N0G0qGfRxBELmWbxp2Ai7LMnfeeSezZs0iPz+///7TTz+dX//616SmplJWVsb999/PySefzObNm9FqtQCcccYZNDU1YTAYiI6OPuzxlyxZwquvvspbb73FZZddNqTYrFYrVuvItoPqO99In3coPvjgA8455xwiIiJcFqdGfXxfY/v2P97j+DvxOjqHf7+OEtrY/F/cN/B1qKv4hB7DFvR6PQWz/kFAUMoh27jrNYyOzaehTIXdXEFMwmSq9uqxmcvRqMcP2K7LWIGpc9shc2K0Gum4Y1bI7ej1eoJDY8V7o4foe/3c9Vk91PN6Wq4lRsCdR5KHOUPk5ptv5vPPP+f7778nKenIl+rq6upITU3l3Xff5fzzzz/mcdPS0rj99tu5/fbbWbZsGa+++ipFRUX88Y9/ZNu2baxevfqI+xoMBkJDQ3n77bcJCAgYztMSBEEQBEFwCaPRyKJFi+jo6CAk5NhXizwl1+rLr175qp2AQOdf5TJ2G7h6ftigXxdfMKwR8FtvvZX//ve/fPfdd0f9gwCIj48nNTWV4uKh90W98847eeGFF3jhhReGtN/8+fNH/H+g1Wpl5cqVzJs3D7VaPaLnHowvv/ySCy+8kC1btpCZmenScx1Pn1WNWubGRQaWvx2Cxep/fUGdRbyOziFex4Fs1i5k2YEkqdj0za8AmHzqB6hURx7wcMdraLN2sXP9LegCkxg9uXexMYfDyo7vb0AXmMyoSUtdHkO3YT87198KQPb4e4mMm3VcxxN/i87R9zq667PaYDAMeltPz7WE4zOkBFyWZW699VY++ugjVq9eTXp6+jH3aWlpoaqqalgLvgQFBXH//fezdOlSFi5cOOj91Gq125Jgd577aL799lsiIyPJzc09bB2nMznjw8FilcSHjBOI19E5xOvY2xt8/ee978PjZr/E2NnvoFIH45CVWAZxVX0kX8N9W5+j09BM5vjHDjqnhuiUS9i39WEiErYSFjXRJeeWZZnta6+j2/BzIqQJGOW05y7+Fp3DXZ/VgzmnJ+daogTFeYZUcX/zzTfz5ptv8vbbbxMcHEx9fT319fX09PS2WOrq6uIPf/gDP/zwA+Xl5axevZqFCxcSFRXFeeedN6wAr7vuOkJDQ3nnnXeGtb/Qa9WqVZx88skuT75B9AQXBG/T3ryVvZuXYuqu7b/PbGqmqvh11n02l3WfzWXXD3cQk3wGsclnEhiShVoTdsSOIu7U0bKdppqvyMj/HbqAgclIVMJJhEQUULjxPtqbNrnk/JIkoQv8ebRSH5SCVn/4+ltBOByRa/mHISXgy5cvp6Ojg7lz5xIfH99/e++99wBQKpXs3LmTc845h5ycHK644gpycnL44YcfCA4OHlaAarWahx9+GJPJNKz9hd5vxtu3b+fkk092dyiCIHiQzva9tDdvwdRdQ0vdajZ/uwiLqQWAfVuWUVn0cxvCyPi5ZI/7I1nj7kKSPLdbgvJAP3KLue2QxyRJQd7UxwiJyKdw0xIcdrNLYhg16UGSsnsntFlMzYfdptuwn02rLmb3T3dhPsI2gn/y5FyrbwTcFTd/M+QSlKPR6/V8+eWXxxVQeXn5IfddfPHFYuGY47BmzRpkWeakk05ydyiCIHiItsYN7NnwRwCmLVhB2Z7ncdh7aG34gbjUs8gev5jm2tVExE5HH5Q6IlfPjkdTzTeUF76IxdQIQHPN1yRl/uaQ7ZSqAGKSFtDetNGli/MkZl5MdfEbqNSHT4ha6r7DbKzDbKyjovBFcibc57JYhEOddtE6Vn04191hHJbItfzDcfUBF7zDqlWryMzMJCUlZcTOKRblEQTPU7H3n1SXvEVS9uXUl38EgD4wGaVCw+jJD2O1tBOVcAoAuoB4krK858NYodT0J9+SQk32+HuPsnVfguO6LxUqVQAnnP4FdpvxsI8nZv4Gk7GGppqvaapZSULGhQSFZrssHkFwBvnA0vGuOK6/8dzriILT9NV/C4Lg37o7eheE0eqimTD330w59f+YeNIbSAoVYdGTiU481eNHuo8kMm426WN+B4A+MJGerkqqS97G2FVxyLZqbThw5PIQZ1EqdWi0EYd/TKUne9w9qDW9sZRsf/yYI5+CIPgOkYD7uLq6OgoLC92SgIvJmILgWfKmPc7Ms1YTl7oQjTYCjS7S3SE5VUL6+Yyd8SwKpZ6iLUup2PsSDRWfDdimq2MfFXtfBkmB8ijtE0eCpFAx5oQnAeg2lNDRss2t8fgbcZV26GRZctnN34gE3MetWdP7BjN37lz3BiIIgjACQiIKGDdrOZNPeZ+QiAIaq7/E0LoTi7mNmv3vsn3tDdhtPYyZ+oRHfAEJDMkic+zvAdj94x30dFe7OSJBODIxCdN5RALu4/bt20d0dDRxcXHuDkUQBGHEaPUxjJr8MLrARPZuup+NX19A+d6XiEmaz4Q5LxMQnI7VMvhFUVwpNuVMdAGJAGz59lLRFUUQ/IBIwH1cRUUFqampbju/KEMRhMFb/MV17g7Bp6g1oaTmXo3VYiAkooCo+JOxmtvY/O0lbPz6AjavupiujqGvHOhskqRgzLQnURxoobh19eUe8+VAEA7mkF138zciAfdxlZWVbk3ABUEYvEcXvOTuEHxOWPRk0kbfiKFlG4bWbdjtPUTEzUKtjcBu62b72mvpaNnh7jDR6CIJi5oEgN1mpGjzUvcG5CdEHbjgLiIB93HuHgEXBEFwt6jEk1Ao9YRGTmDsjGcJDMnCam7tf7xk+2PIDpsbI4Tasg9obVjX/3tHy5YhdUWxmtupKn6dmv3vuiI8QQBEDbgziT7gPszhcHjECLjoCS4IgjtpdVFk5t9G8fbH0Aclk5hxESZjHW2NP5KYcRH7tj6MsauSwJAMt8UYGjme2JSzCAzNRqONQqFQDakl5P5dT9NS1/s+G5VwMlp9jKtC9TmzFq4R5ZLCiBMJuA9rbGzEbDa7PQEXBEFwt+ik0zAZ66gs+hcBwZmk5l5Fau5V2KzdAHR37HNrAh4cPobg8DHD3r+rfe+BnyTUmlDnBCUIv+Cq0Wp/HAEXJSg+rLq6t51VcnKymyMRBEFwL0mSSMn9LfqglAGlHip1IGpNmFe3/3M4bJh7GgDIyL8dhVLr5ogEQTgWkYD7sLa2NgAiIg6/EttIEpf3BMH5RNeUoQuPOYHW+u+xWTr779MHp2HsLHdfUMdJoVAxesojZI69k7jUs90djlcSZZKDI7qgOI9IwH1YR0cHAKGh4nKkIPgi0TVl6GKTz8BmNVBT9p/+++zWbiTJuz8OI2JnEJd69pDqxgVBcB/vfscRjqqiogKAkJAQN0ciCMLxECPdzhMQnEZgSBaGlm00VK6grvwjug3FxCQvcHdogpuJUfBjE11QnEck4D6su7t3cpFC4Rn/m0UZiiAMjxjpdq6QiAK6DSWU7HiC0l1/QxeYTHjMNJee02btHHSrQ1mWcTisLo1HEIbD4XDdzd+ILig+TKvVEh4eLi5JCoIgHCQj/3dk5P8O2WHDZutCodAiSUqXnc9uM7J51SWo1EGk5F5NVMLJR31frtj7T2r2v83U+f9FrRFXMEeSaEkojBTPGBoVXKK+vp7Y2Fh3hzGAeGMTBMFTSAoVak0YSpXepefpaNmGzWrAZKxl39aHKd72CHab8Yjb93U0Kdy42O0LBAnCwUQJivOIBNyHNTQ0eFwCLgiC4G9CoyaRNe5uopNOA6CpZiWbVy3CYm477PZJWYsA6GzbTV35xyMVpnCAqAUXRoJIwH2YpybgYhRcEAYSkyx9m1KpJTb5dHLGL2b0lMcAsFra2bjyPIq2PIQsDyyADQzJJC71XABqSt8d0pL0gnOIJPzwxAi484gE3Id5agIOIgkXhIOJSZb+IyL2BKbO/5iohFMAaG/eAhyafSRnXwaAxdRMS/13IxmiIAgjQCTgPsyTE3AQSbggCP5JrQkjd+L9zDxrNdPmf3LYCaAaXSTpeTcDULT5waPWjAvCSHHgooV43P3E3EAk4D6stbWVyMhId4dxVCIJFwRBOLz4tPP6f+5o2e7GSPyTKEMRXEkk4D4sICCAnp4ed4dxTCIJFwTnslk66WzbQ0fLdnq6q7HbTe4OSRgGU089Gl0UAJ2tO90cjX8SSfhAsiy77OZvRB9wHxYSEtK/HL2n+/7TE8UbnSAMweIvruuvHe82lNBY9SWd7Xvo6arCZjUcsr1SHYRWF3NgJchMAoLTCQhOB8Bm7cJm7aKnq5z2ps0kZV9KcNioEX0+wkCybGfHupuxWXrfw9uaNwEXuTcoPyV6g//MVRMm/TD/Fgm4LwsNDcVgOPSD2FN9/+mJWK1WVqxY4e5QBMHjPTT3UZrL/o/Gqi/oNhSj1oQTFj2Z8Jhp6AKT0Acmo1BqsJhasJiasZiaMffUY+wso63xJ+y27sMcVQJkOtt3kzf1cSymFjpatmLuacJhN+FwmHHYD9wcFsKiJpM2+gYUSs1IP30/oCAu9Ryqi98AZEzd1QCU7n6OhKwbUSjU7g1PEITjIhJwHxYaGuo1I+CCIAzksJsxm5ow9zRg7mk86L/1mI31mIy1SJKS8JjpJOdcSXjMNBSKQ9/SA4JSD7lPlmUspkaMneVIChUqVRBKdRAabQTGzjKKtixj+9re1ogaXTT6oBSUSh1qTSgKhRaFUocs26mv/BSLqZnMgj+IFRudTJIkUnOvIi5lIc2139DVshaAxqr/YWivZdSkh1AotW6OUvA3souWjZf9cBamSMB9WEhIiFeNgB/sy/dmctL5690dhiCMGIu5jaaalbQ3bqC7sxSruXXA42ptOFp9HFp9DIFxWQSF5hIWPQm1JmzI55IkCa0+Fq3+0C5JweF5jD/xX3S27kQXmIguIPGIy6aHx5zAvi0PsembC4lKOJnIuNnoAhKQZTsmYy0KhZrQ8GQgYMgxCr20+mgSM3+DZtRFwIFylMYfqSv/mMRMUZIyUkQZiuBsIgH3YQqFArPZ7O4whk3UhQu+TnbYaGvaQEPlCtoafwBJQVjUJGJTzkQXkIBWH9ObKOuiR3S0U6UKIDxm2jG3i4ybyaRT3qO+4hOaqlfSWHVo+ZheHwg3voWpp54eoxFZthMUmuOKsP1CUOgoenq29i9XL4wckYSLGnBnEgm4j2psbGTt2rXcc8897g7luIgkXPBFxq5KGqv+R2P1l1jNrQSGZJOedzNRiad6XSmHRhtOSs6VJGdfgclYi9XcAkj9I+FdresA2Lbmqv6uTDPO/LZ/VF2W5SOOsAuHysi/lab6q47a2UaWZerLP0IbEE9Y9GRRLy4IHkgk4D7qkUceQalUcuONN7o7lOMmknDBF9hsRlpqV9NQtYLOtl2o1MFEJ84jJvl0gkKz3R3ecZMkCX1gIvrAxAH3B6edC3SQM2EJezb9BauljeJtfyYu7Tx2rutdaCZz7B+ITTkDSRKdcY8lIDidgln/QB+QeMRtTMYaSnc/C0BgaA5jpj0xrFIl4VD+Pgret3COK47rb0QC7oNsNhv//ve/ueWWWzx+IZ7BEkm44I1kWaazbScNVf+jufZbHHYzYdGTyZ34IBGxM4ZVVmK39eBwWHqTVUmJJCmQUCAplIDCY0eTI2JnMOmUd2ms/Jzq/e/QVPM1ASGZGA372b/zLzRUfkr2hPsICEpxd6ge71gtIjW6KBRKHQqFhu6OfWz46lyCwkaTOupaOpq30N60kZjkBQMW+hEGz9+TcME5RALugzZt2kR7eztnnXWWu0NxKpGEC95ClmWaalZSVfwGpu4qtAHxJGUtIibptMNOfBzcMR3U7H+Hyn2vITusR9lS0ZuUSwqQFEiSEklSolBo0AbEEhk/l/jUs93SQUOp1BKffj6xqQsp2/08DZWfEpdyNvWV/6Wro4itqy8nJfcqkrMvH/HYfIlSqSMl50rKC//Rf19XeyGlu/5GT1cF0DsyLgyfvybhogbceUQC7oO+//57dDodU6ZMcXcoTtf3hicSccFT2W09FG1ZRlvjD0TGzSFz7J2ERo47rvIKi7mN0l1/o6VuNQkZFxESno8s2zln24t8PO5qZNmOLDuQZTvIjgM/O+Cg+x12M8aucioK/0Fz7SrGnPBXVCr3dCdRKNSkj7kFY+d+WhvWUzDrHzRVr6Su/P/oaNlOsvdX5LhdYuZvSMi4iK6OIpprv0WlDqSnq/rAoksGJEnp7hC9nj8m4bJDRnZBvYgrjunpRALug1JTUzGZTLS0tBAbO7zRNkEQhs5qaWfPhsUYO8sZPeURImJnHNfx7HYTtaX/oWb/O4DEYkU8Myu38mhe79yOOTveZl3iqUM6Zmf7XnZ8fwNN1SuJTzvnuOI7HgqFipyJD7J97bUUbV5K7sT7SRt9/aD3b6z+itrS91CqgtAHpSA7rJh7GtEHpZA2+nqUKr0Lo/cOkiQRHDaqv2TF3NNI0zdfAb2tDAVBcB8x48UHxcXFAb2dUHzV95+e6HcjD4JnM/c0sXPdrZiN9Yyd/sxxJ99mUzM71/+Oqn3/Jjb5TCad/DbfnfEOjy54icVf9C6S07cU/WA47GY62/bQ1vjTccXlTFpdFAUzX0CjjWDHulup2PtPHLJtUPs2166i27AfQ+t2Gio/pbH6C4ydZdRXfMyeDXcj++M17WNwOKxodNEAqDWhg97PYmrB3OO7nyfHw9+uxvZNwnTFzd+IEXAfNH78eJRKJevXr2fs2LHuDkcQfJ7d1kPhpvuw202Mnfkc+sCk4zqeuaeRHetuARwUzFp+SJeUwSbeXR37aK79lvbmzRgNpciyDUmhJiH918SlnHlcMTqLLiCe/BnPUlv6PlX7/k1TzUri0s4jNHICKnUQDocF2WHF4bCC7EClCSUwJJOcCUuoK/+Ilro1dBtKALBa2gAwtO5g/Yp5aDRhJGb+hvj0Czx2cupIaW1YT9GWZTgOtC/MmbBkUPvJsp2d63+HyVhDQHAmQWE56AOTCI2cSFBYruhcg3+WogjHTyTgPig4OJipU6fyzTffcP31g7+k643ExEzBE1Tue42erkrGzji+5FuWHbTUfUfZnheQJImxM15Aq48Z1rFMxnq2r70eSaEiKv4kYpNPJyh0FIEhmSiUmmHH6AoKhYqkrEVEJZxMdcmb1Ox/h6p9rx5xe21APDp9PLqAeHImPoA+MAmHw4rRUIrV0kHp7r9jNlajUOko2/N31NpwohNPGcFn5Fmaar5m39Y/9f8+atLD6IOSB7WvoXUXJmMNAMbO/Rg79/c/FhQ6iuzxdxMQnO7cgAWPJSZhOo9IwH3UKaecwvLly3E4HCgUvj1CIZJwwd0MrTuIjJtzXP28e7prKNr8IN2GEsKip5JV8PthJ98AWn0sCRm/prb0fTS6KGJTzhr0gix2uwnZYUelDhz2+YdDFxBHVsEfyMy/HWNXBQ67CUmhQaHQ9H9pMBlraalbi7mnntbGH2moWkFk/BySsi4hOHw0AKGRBRRuvI+Olu0A7Nv6MK0N68ke90e3dH9xF7vdTGXRv6gtfQ8AhVJHau7VRMbPHvQxgsPzyBh7Bz2dlUiSArvdhMlYS0fzZro69lK66znypz/lqqfgNfo+g8RIuDBYIgH3UbNnz+ZPf/oTpaWlZGVluTsclxNJuOBOPd1VRMROH/T2siz3l00EBKfR3rSJkh1PoFQFMXbGs4REFAw7lsVfXMejC15CkiTSRt+IWhtBxd6XaG1YT0b+rQSHjUah1B21dKBo04O0Nf3ECQtWoHRDpxRJoSIwJPOwj+kC4gmLmgSAw26hsforava/zfa11xEWPYWkzEWERI5n1JQ/01L7LeWF/8Bm7aS59hvUmhCSs69ArQ0bwWfjHuaeBrZ9dy02qwGApKxLSMz8DSp18JCOo1CoiU89dLJuW+NP1Ox/l9RRVzslXsE7OBwyDhcUbLvimJ5OJOA+Ki0tDYDq6mq/SMBBJOGC+wQEpdHVXnTYx6yWdmr2v4fF3IrD1oPd3oOpuwaTsRYASVIhyzaCw8cwevKfjzs5PLg+XJIkkjJ/Q3j0ZEp2/JXdP/6+/zGFQotCpUOp1KFU6YlJPpOE9F8BDtqaeidq1pZ+QHKO5/bkVig1xKWeRWzyAprr1lBd8ja7fryD4LA8ErMuISb5dEKjprB51YUA1JV/REfLdgpm/t0tXyxGUkPliv7ke/SUx4iIPcGpxw+PmUZ4zDSnHtMXiHpwYbBEAu6jEhN7lymurq52cyQjSyThgjtExM2iquhV7HYTSqWu/35ZlinasozOtkKCQrNQKPUoVQG9yUvsDCRJSVfHPsKiJhEYkuWyiYKBIVkUzHweQ+suLKYmHHYTdrupd1VNuwmTsZ7yPc/T2banfyKjPiiN+srPiE6ahy4g/ojHtlo62LflYVJyryI4PM8l8R+LpFARnXgKUQkn09b4E9Ulb7J3030EBKeTNvomYlMW0lD5KQDGzlLqyj8mLvWcES+xGUnhMdNQqgKIST5NLEM/wnw5CRc14M4jEnAfFRgYSExMDM8//zyXXHKJX3UAEEm4MBIs5jZa69cBYLd24XCY6WzdRVj05P5tjJ376WjewqhJy4iMn3PY44RFTRyReCVJQWjkkUtbQiLGUFv2IRptBKOnPEJAcDo71/+OLd9eSnB4Hip1CA6HFdlhQ5Zt/T+bTY3YLB20N2+iYOYLbkvCoXfEPyL2BCJiT6CjZQeVRa+wZ8PdpI66lvzpz7Hrh1sBqNj7EhV7/4kuMJHY5DNIylrktphdJTg8z63/L/ydrybhIgF3HpGA+7CmpiYaGxt59913ufjii90djiB4PVl20NG8hfrKz2it/753tUkAHETGn0hwxMC2ny11a1GqAgkfQn24u8SnnUd82nkD7ps499801XxNe/NmHHYzCqUWhToQSVIjKVQoFGpCFAVodTG01K9lz4Z7GDf7xaOOmI+U0MgC8qc/RWXRq1TsfZGIuBPRBSZh6q4mPOYEgkJzqSr+N60N63wyAfdUvf3ZZdG+UPB7IgH3YXfeeSd//etfWbRoEWeddRbBwUObfOPNxCi44EwWUyvVZV9QX/kZZmNd//2hkRNIHXUNgSFZKJRa7HYzLfXf0960AWNnBZ3te4hJWjDo7iO/1G0opWTHkyhVenQBiegDEwkIySQsauKILCWuVAUQl3o2calnH3PbmOQF7Pj+Rgo33kve1CfQ6qNdHt+xSJKS1FHXEBSaw75tj6JSBxMZfxJtDev6V4KMiJ3l5ij9R33FZ+zf+RcUCg3j5rxMQFCKu0NyKV8cBXfIMg4XDFe74pieTnwF9WHz58/v//mOO+5wYyTu4WtvfIL7bPvuKir2/rM/+ZYUakIiCujqKKK57jsAakv/w+ZvfsPeTUtob96KRhdFcvZlpI+5ZVjntFk62btpCXZrFypVEF3te6jc9xp7frprQE9nT6HWhDJ6yiPYrF1sX3st9RWf4bCb3R0WAJHxc5gw5xV0AfG01H1LUGguIRHjiE1ZiMNuYt1ncyncdD+mg75cCcfH2FWJsbN8wH1WSzsADoeFmv3v0lTzDe3NmzEZ63125VIxECQciRgB92GzZ89Gp9NhMpl45ZVXuPHGG5k0aZK7wxpRYiRccIoDl8sVSh0JGReSmPFrVOpgNn3zG2pL36O55hssllZikhaQmHkRAUGpx33KyuLXsFo6GD/n5f6SDlmWqa/8L6U7nyY6cT5Pbn11SMvRu1pAcBrjZ/+T/bv+xv6df6Wi6J8kZ5wBXOTu0NAFJpA//Wla6r6jpvQ9utoKsVo6+pdkb61fS0fLVrLH3UNknBgVP16FG+/tLfeJnQEOOwmZvyEp6xI0ukhKtj9OY/UXNFat6N8+MfNi0kb75sJxvjQSLjt6b644rr8RCbgP0+v1zJkzh6+++gqAyZMns3v3bvLyxMQcQRiKcbNepKWpkNCIcQPaBDocFgBCoyaQnH0F+qDjW4L+YHZbD0qlHo02sv8+SZKISzmLjqbNFG15iKUnvYmnLSuj1oYxatKD9HRdRX3lp7TUfQtcxM71vyMoYibhMdPQB6W5ZWK4JCmISphLZPyJdLbtpGb/e/2lKAAh4fns3bSEpOzLSMm5yq8mrzub3dYDQFvDegACgtMJi5pAbPLphITnU1fxMXVl/9e/fUPlZ6SOuk685oLfEAm4j5s/fz7ffvsto0ePZseOHYwZM4Yvv/xyQHmKrxOj4MLx0uqjiYo/dFXKcbP+gUKpcUmbt8SMi2is+oK6ik9IzPh1//2SpCRr3F1s/PrXNFR8Skrub51+bmfQByWTnncTKuW1QDcaXRSVRf+ivPAfaHQxva0Yo6cSGjVxxNsBSpJESEQBIREF2G09dHeWEhCYglIdRM3+t6nY+086W3eRmLVoxOrtfU1m/m10tGxDo48hInYG+sDk/sf0QclkjLmV9LybsNvN1JV9SGhkgU8n374yCi4ju6RcSMY3S5CORtSA+7jTTjsNq9XK0qVLSU1N7b/v7rvvxuHwn2s+vvDG5w0MrTuo2vc6deUf01K/Dou5dUj7m4z1VBa9Sl35R5h7mlwUpfNo9TFOT75tlk4qil5BpQkmNuVMqotfx2bpHLCNSh1MTNJp1Ff+F4fD6tTzO5tC0TvOkzvxAaae9il5Ux8nMm42HS1b2bv5fjZ8dTY7f7id6pJ36DaUHvbD3eGw0m3YT2P1V1TsfZmGqv9hMbU4JT6lSk9I+BhUmt5J6kp1EBEH4tvz010UbrzPZ+uTXSkyfg4Z+b8jKfM3BASlHDa5liQlKlUAydmXHtfqr4LgjcQIuI8bM2YMqampvPXWW2zYsIE5c+ZQVFTEE088QXBwMEuWLHF3iIKXMfc00Fq/jo6WbVgtHUiSEklS4HBYMLTuAHo/WGXZDoAuIJGQiLEEh+cREJxOUNio/q4gsiyzd9P9tDZ8j0oTis3S0b9/+Z7l5E19nNCoCe55om5iaNtJdfEbVBe/QWTcHGzWTlrqvyM25cz+bWRZRqkKxGpuw9RdTUBwuhsjHjylUnvQCoq30tNdQ3vTBtoaf6Jq32tU7H0RjTaKkMhxaPUxWExNdBtK6emq6P97UmsjsZpbUSg05Ey474j91YfDYe+hdOfTA+5ra/yRHd/fQO6kpR7RXtFdZFmmqWYlVnMb8ekX9H+xEvyL7ABXjN2JGnDB50iSxCOPPMIll1xCUlISa9asIS4uDoANGza4ObqRJUpRjo/N0klVyRvUlX0I9C70odHFAA5k2YFKUpI66lri03+FQqHGYmrG0LaLztadGFp30lj9FeBAqQogLHoqEbEzCArLxdC6/cDxOw4cdwx5Ux6jaMtD7Nl4L/kn/NWvFhQJi5qMWhOO1dJ2IPE+i6iEk4De/wdNtatorFpBV0cR4TEnoNXHujni4dMHJqIP7O0/7rCb6WjdQXvTRjrb9tDZtguNLorg8DHEpZ5NYEgmAcHpqNRBWC3t7N/5N/ZufoCcCfcTnXiKU+JRqgKYeNJb7Fx/K1Zz64Hl6iWsVgPb1l5Lzvh7iYid4ZRzeZvKopepLnkLgPbmTYyZ9qSbIxLcQZZdVIIyjGO+8MILPPnkk9TV1TFmzBieeeYZZs+efdhtP/zwQ5YvX862bdswm82MGTOGpUuXctpppx1v6MMmEnA/cPHFF9PQ0MC9997L//3fz5Ne8vPz3RiV4Olk2Y65p5Gu9iI6WrbQXLsah8NCcs7lJKT/6kBycmRafQzR+pOJTjgZAIfdgrGzjLamn2itX0/xtkcOu19gSCYqTTCjJj/M7p/uYtePdxKfei7JOVegVOmd/jw9jUKpYdzsFyncuJhuw3462wop3vYYdnsPHS3bkGU74TEnkDfqScKjp7g7XKdRKLWER08Z1HNSa8LInXg/RVtkynb/nfCYaajUQU6JQx+YSO7Epez64TYUSh15Ux9Hp4+jePtjFG68l7jUc0kdfR2qY/z9+xqbtbv/59DI8e4LRBCA9957j9tvv50XXniBmTNn8uKLL3L66aezZ88eUlIO7S//3XffMW/ePB555BHCwsJ49dVXWbhwIT/99BMTJrjnKqtIwP2AJEnccccdrFu3rj8BlySJM844w82RjTwxCj6QLMtYLW30dFVj6q6ip7uanq7e/5qMNcgH6ot1gclEJ80jKXMRGl3kMY56eAqlhqCwXILCcknOvhyzqRmjoRSLqQmzqQmLqZnAkCxiU84Cemtz86Y+TnXJm9SUvodGH01C+gVOe+6eTKuPYezM52mp+46O5i1YzC0oFBpSc68mOnHesP8f+BJJUpCedzNbvr2U+opPSMq6xGnHDo0sYNqCz1FIKhRKDQCjJv+JuvKPKNv9LK0N60jLu5HAkCyfX0ymT1LWJQSH5xERM72/Xl7wPw659+aK4w7FU089xdVXX80111wDwDPPPMOXX37J8uXLefTRRw/Z/plnnhnw+yOPPMInn3zCp59+KhJwwfUWLlzYn4DffPPNzJolet36C5u1G1N3dW+C3V01IOG22/pGtiS0AXHoA5MIi5qIPvBsdIFJBIRkoNVFOT0mrS7qmMdVqQNJG339gdrTDmRZ9ulOCQdTKnXEJM0nJsl/OhYNlVYfTUTcTJprVzk1AQcOGeGWJImE9PMp2/0sFlMT+7YsAySyx99DdOJ8n/+77Omuwmpu9ctuFcLIMRgMA37XarVotQObrVosFjZv3sw999wz4P758+ezfv36QZ3H4XDQ2dlJRETE8QV8HEQC7kcWLVrElVdeCcDUqVPdG4wb+dMoeHnhi9RWfIu5p6H/PrU2HH1gMgEhmUTGz0UflIQ+MBldQDwKpad1le6lUGhpbVxHQ9VnTD75HY+NUxh5kXFzKNqyFHNPI1r9oa0inS17/L0oFBpCIgso2/08xdseZf/OZ1Cq9EiS4sAqm2MJiRhLUNhoJMn7m431dNdQuPFeHHYTsiyTlHWxu0PySrMWrmHFW949qVx2yMguGALvO2ZycvKA+x988EGWLl064L7m5mbsdjuxsQPnv8TGxlJfXz+o8/31r3+lu7ubCy+8cPhBHyeRgPsRtVrNCy+8wE033cS4cePcHY5b+WoSLssyhtbttNZ+AlfcRkvdaiJiZxIcnocuMAl9YJLTamVHkkoTgsXUgtXcSlPtKmKTT3d3SIKHCA4fDUC3oWREEvCDr0hkj7+HqIS5mLprcNgtOBxmOtsKqdz3Gg67iYDgTFJyriQibpbHj5BbLe1Ul7yNqbsWlSaE6MR5hEaOo7NtF/t3PoVSqcdhN6FxwdUwQehTVVVFSEhI/++/HP0+2C//TQ32Cuk777zD0qVL+eSTT4iJcf17xpGIBNzPXHXVVTzyyCM88cQTvPnmm+4OR3ASWZZpqVtD9f636e7YR2R0LgATTnwNm0Pn5uiOX2zymezf+ZfeLhgq7/sCIbiORheDSh1CV0fxiHcoUSjURMYd2nXB4bBhaN1BdfGb7N18P4EhWYRFTUajj0ajjUSh1CIpVKhUQQSFjXJrcm6zGTG0bKOy6FVMPXUEh43G0LKtd5l4SQGyg4DgTKKT5lNb+h7BYaPdFqvgfrLce3PFcQFCQkIGJOCHExUVhVKpPGS0u7Gx8ZBR8V967733uPrqq/nPf/7DqaeeelwxHy+RgPsZrVbL3XffzW233caSJUsYNWqUu0NyG18ZBTf3NLJ/51O0Nf5IWNRk8qY+QUzCZMDQW6rhA/1V1ZreN+TRk/+MLjDBzdEInkSSJAJDs+nuKHZ3KP0UChVhURMJi5pIR8s2ava/R0v9d5hNTf0Tm/sEh+eTVfAHAoLT3BLrvi3LaGv8EW1APGOnP0tgSAayLNPZtotuQylafTRBYaPY/t11hMecgD4oyS1xCkIfjUbDpEmTWLlyJeedd17//StXruScc8454n7vvPMOV111Fe+88w5nnnnmEbcbKSIB90PXXHMNTz75JEuWLOGDDz5wdzjCcWip/57ibY+iUOoYNfnPRMbNBECSfGuiVGvDevRBaSL5Fg4rJDyfmv3v0NGyzeNa5IVGju+PSZZlbNZOZIcVWbZh7CyndPdz7N28lAlzXkZyw+I2NmsXgSHZjJv9Uv9IvCRJ/XXsssPGno2LkWUbmQV/GPH4BM/icMg4XFADPtRj3nnnnVx22WVMnjyZ6dOn89JLL1FZWckNN9wAwOLFi6mpqeH1118HepPvyy+/nL/97W+ccMIJ/aPner2e0NBQ5z6ZQRIJuB/S6XQsW7aMK6+8kg0bNvj1hExvJssy5XuWExQ2ilGTlqJS+2ZrMLvNSEv9WuLTz3d3KIKHSsxahKF1J7t+vJPYpNNJzrncIxcokiSp/2oOgFYfS1jDeuor/ovZ1OSWlTat5lYsphZqy/6D7LDicFix23owddfQ012FyViL7LAxZtqTLumGJHgXT1mI56KLLqKlpYVly5ZRV1dHfn4+K1asIDU1FYC6ujoqKyv7t3/xxRex2WzcfPPN3Hzzzf33X3HFFbz22mtOeQ5DJRJwP3XppZfy5JNPcs899/DNN994/AQhV/HmMhRjZykmYw0Z+b/z2eQboKnmG+w2I7HJ7r9kKHgmpVJL3rQnqCv/kJqSd2ip/47cSQ8RFjXR3aEdlclYR33Ff0nKvsxty9yn5d1EyY4nqdr3bySFCoWkRqHUogtMICxqEvrAcwgKzyM4zH/LFQXPdNNNN3HTTTcd9rFfJtWrV692fUBDJBJwP6VUKnnkkUc455xzWLlyJfPni17D3qa5bg1KVSCh5d4BCAAASVBJREFUHp5kHA+btZuqff8mMm42uoA4d4cjeDCFQkVixoXEJp9O0ZZl7PnpLjLybycudaG7Qzssq6WDhqr/ATLVxa+TlHUJSje014yMm0VknFgTYqTMv/B7d4dwXGRH780Vx/U33t+gVBi2hQsXMnXqVP72t7+5OxS3+v7TE90dwpD1dj35joi4WSgUaneH4xKyLFO2+zlsti7Sx9x87B0EAVCpg8mb8ihxqWezf+dfKdnxV0zGwfUGHknF2x+nuvh1VJpQYpJPxz+vQQqC/xIj4H5MkiROOOEEPvvsMxwOBwqF+D7mLTpattHTVU563uEvv/mCmv1v01j9Bdnj7/XIel7Bc0kKFRn5t6EPSqO88B80VH5GYGg2CoUGSaFCrQlFrQlHo41ArQ1Doext1anWhKELTEQXEO/yBXTs1t4VaG2WTppqvqazbTcabSQqTRhqTSh2mxGruRWVJpSAoFTCItOB3kmR4JtfugXP55BlHC6oAXfFMT2dSMD93Lnnnsuzzz7Lq6++ytVXX+3ucNzG22rBO1q2oNZGEhY9xd2huERTzTdU7P0nydlXiKXYhWGLTzuHmKR5NNeuxtC2C2Q7DocVq6WDnq4qrJY2rOYOftmrU6kOIiSigJSc3xIUmu2S2DILfk9D5WfoA5NxOKyYumuwWlqxWjowdVeiUOrRaCOwmJpob9pIU5UVeIdN3y5CHzKRpKxLCAhKcUlswuAM5+qpwWDATU03BA8jEnA/d9JJJ3HFFVdwxx13UFBQwJQpvpnQ+ZqezgoCgtN8bvKsLNup2vc6VcWvE504j+ScKzEZ67CYmjG07iIqYa7bJqsJ3kmpCiA25QxiU8447OOy7EB22JCRsZpb6emupqutkMbqLyjasoyJc193yb+zgKCUQV/BkmUZSW4HID7lbCpK/ktT9UpikuaTOupaNLpIp8fnL7yxBNGdPKULii8QCbjAc889x759+zjttNNYtWoV48ePd3dIwjGYjDUEh49xdxhOZbV0ULRlGR3NW0nJvYrEjIso3fU36is+7t/GbjeSmuu/V2oE55MkBZJSA4AyIB5dQDzh0VMIiRjLrh/voGz3c6Tn3eSWHt0/xyih0YQBHSRlX0pU8oXUV35OdfHrtDX+RO7EBwiNmuC2+DzBYBNpq9XKihUr+PK9majVopRHcB+RgAsEBwfzv//9j1NOOYV58+axevVqxozxreRuMLyqDEVSujsCp5JlB/u2/onujmLGnPAkYVGTqCl9n4bKT0nJvQZD63bamzYRneDepYMF/xEaNYGM/Nsp3fU31NoIkrMvdXdI/RRKLQnp5xOVMJd9W/7E7g1/JHfCA0TGz3Z3aE4jRqY9k6csxOMLRAIuABAaGsqXX37JySefzKmnnsqaNWvIyclxd1jCESgUGhx2i7vDcJra0v/Q3rSRvGm9ybcsy9RXfEpk/EkEBKdSWfQyqaOuJyA41d2hCn4kLvUc6so/xthZ7u5QDkujjSBv6mPs2/YIezc/SOqoq0nMuMito/VHI5Jq7yfLvTdXHNffeOa/UsEtIiMjWblyJXPnzuXkk0/mu+++IyMjw91hCYchSUpk2ebuMJyis30vFXv/SWLGbwg/MKnUZu3E1F1FVPwcirc9SmTcHBIzf+PmSAV/Ist2Kva+cqDb0I3uDueIFEoNuRPvp6Lwn1TsfZm68k+ISV5wYIKmREBwGoEhmS6NQSTWgjB0IgEXBoiJieGbb75hzpw5nHLKKXz33XckJye7OyzhF+z2HhRKvbvDOG42m5F9Wx4mMCSTlFE/13arNSGo1CFUl7yFPiiVrPH3+NyEU8FzGbsqKNn+JJ1tu0kddT3hMdPcHdJRSZKStLwbiEo8hbryj6gv/wibtbP/8fS8W0jI+NWwji2Sa+Fgsiwju6BcxB8nYQ6p0emjjz7KlClTCA4OJiYmhnPPPZeioqIB28iyzNKlS0lISECv1zN37lx27949YJuioiJmzpxJUlISy5YtG/BYWlpvZ4cff/xxwP233347c+fOHUq4wjDFx8ezatUqAE4++WTq6urcHNHI8ZYPG7utB6XK+xPwisKXsJhbyZlw/yELCiVlX0pUwkmMmfYkKlWAmyIU/InDYaWq+HW2fXcNVnMb+dOfISnrYneHNWhBodlkj/sj0077lGmnfc7U+R8Tk3wGlfv+hcNhPWT77z898Zg3YeSJXMs/DCkBX7NmDTfffDM//vgjK1euxGazMX/+fLq7u/u3eeKJJ3jqqaf4+9//zsaNG4mLi2PevHl0dv78bfzmm2/msssu45NPPuHTTz9l3bp1A86j0+m4++67j/OpCccjOTmZVatWYTKZOP300zEaje4OSTiYbEfy8omYdruJxuovScy8CH1Q0iGPJ2ZcSO7EB9HqY9wQneBvOtv2sH3tdVTue42E9F8z/sR/ERo5zt1hDZtKHcj6FWex4KQoQoK1rP3vSSK59hKenGvJBxbicfbNH0fAh1SC8sUXXwz4/dVXXyUmJobNmzczZ84cZFnmmWee4b777uP8888H4N///jexsbG8/fbbXH/99QC0t7czYcIECgoKSEhIoKOjY8Bxr7/+epYvX86KFSs444zD924VXC89PZ3PP/+c6dOnc9111/HGG2+IMgAPISlUB1bE815tDT/isPcQnTjP3aEIfszhsFJe+CJ1Zf9HYGg242a96LLFd5ytL4nu7u6mubm5/9bV1YXZbOZXv/oV//3vf/n73/+OUumcL+zV1dW8/fbbNDQ0oFKpuOSSSygoKHDKsYVeItfyD8dVA973PzMiIgKAsrIy6uvrmT//55XrtFotJ554IuvXr+//o1i2bBnz5s2jp6eHs846i9NOO23AcdPS0rjhhhtYvHgxCxYsEEuku1FBQQGvvPIKF198MVOmTOG2225zd0gu5w3tCCVJ5fWTMM099ShVgegDE90diuDHKov+RX35R6SNvoGE9As8qoPI95+eSFtbG6Ghodjt9v7+1R0dHXz77bfccMMNrFq1iuLi4sPuHxAQwAcffMB5553ntJgyMjKwWn8uZ3niiSc48cQTefLJJ8VCbi7iSbmW7HBRDbhoQzh4sixz5513MmvWLPLz8wGor68HIDY2dsC2sbGxVFRU9P9+xhln0NTUhMFgIDo6+rDHX7JkCa+++ipvvfUWl1122ZBis1qtA94gRkLf+Ub6vCPhggsu4K677mLJkiWMGzeOmTNnuuxcnvI6atSe/Wbw/+3dd1hUR9vA4d8uvUoRBZSqGIIVQUWxF+y+xi7EEjVqNBo1RkyssceexCCaqDH25LNHVDR2ook1saBYAQUFQQUEaTvfH77uG8QI6MJS5r6ucwmzs+c8Zxx2nz07Z8bAQAcjQ8N/jfNFeXE+D0MjQwwMdNDVyURZjJKefyoJ7VjcFec2fJLwF4kxO3GrNYxKrj3/W5p3nCpVFpnpiWRnPSUrM4WszKdkZSWTmfaQ9GdxpD+LJ+PZQ7Kz0gCBrq4pOvrm6OmZofvff3X0zTExq8LRPYMxMDB45XEePHiAi4sLPXv2ZPny5QAMHTqUrVu3kpWVhZubG+3atWPKlClUrFgRKysrrKysMDU1xcDAAAMDA3R0dDT6erp161Zmz55Nenq6elzyn3/+yYQJEwgNDdXYcQqLtt9jCnrc4pxrSW/njd/1Pv74Y/7++29OnDiR67GXhykIIXKVGRgY/GuHALCxsWH8+PFMnTqV3r17Fyi20NBQjI21c9PWgQMHtHLcwubr64uv7/MrLyEhIYV+PG234ycDtHr4vA1Y8N8fnry22kf+SYUfyxtr/t/t6eurFQPFux1LhuLZhs7Axv/+/Pq/pdwM/rtZvVUEv/3222sf37RpE/C/18SuXbvStWvXXPXS0tK4d+8e9+7de6t48uPzzz9/ZXlRvDdoirbeYwp6P1Vxy7XkFXDNeaMEfNSoUezatYtjx45RufL/bp6ytbUFnn86s7OzU5fHxcXl+qSWH+PGjSMoKIigoKACPc/Pzw9zc/MCH+9tZGZmcuDAAdq0aVNql7d9+PAhzZo1o2rVquzcubNQjlFc2rFt77C8K2lBUuJFrl+Yg1LHkBo+S9EzKPfKevp6go/8k1i+0ZyMzOI3bl8IQcrjK1z+4zMc3Ydg79xN2yG9UnFvx5KguLbhzUtLib8birGpI0pdU1Blkpn5hPS0h4Dqlc8xNKmMuVVNjM1cMDSyQ1fPFB09E3R0Tdn/cxv09fXzfXyVSsWVK1fYv38/69at4/bt27nqtGnThm7dujFv3jw8PT3p3bs3LVu2xNDQ8E1PO18ePHjAggUL2LFjB/Hx8SiVSpydnbGzs8PW1hY7OzuEENy7d4+7d+8SHR3NgwcPqFq1Kh9++CH+/v4Feg9esmQJ8+bN49mzZ+oyhUKBp6cnderU4a+//uLcuXMIIbCyssLJyYlr166RmppKnz59mD17NuXLl8/XsbT9HpOUlP8PosUx11KJ55umlcH8u2AJuBCCUaNGsX37do4cOYKLi0uOx11cXLC1teXAgQN4enoCkJGRwdGjR/nqq68KHJypqSlTpkxh+vTpdO7cOd/P09PT01ryps1jFzY7OzsmTpzIkCFDePLkSb5f8N6EttuxOCUKLzyMOULE+ZmYW9Wimtc0hNKCjDy+zczIVBS7c8lIT+TGX/N5FHcKpY4Rppa+xS7GlxXHdixpilsb6hq4YmxRH4VCD4VSF4WeHqZm5lgb2WFobIuBsR16+uYgQCBQKJTo6plqdPYQT09PPD09mTBhAnFxcSgUCpRKJUqlEgMDA0xNTQEYMGAAmZmZhISEYGhoWKivjZcuXaJhw4bo6+szZMgQOnbsiLe392u/VRZCEBYWxrJlyxg3bhyBgYG0b9+eHj160LFjx9cm49u3bycwMJBx48YxduxYrKysePz4MQcPHmTPnj2EhYVRrVo1PvjgA1q3bo2DgwMKhYKsrCzWr1/P2LFj2bZtGyNHjsTBwYHs7GwMDAxo3bo1zs7O/3pcbb3H5OeYJSXXkt5OgRLwkSNHsnHjRnbu3ImZmZl6HFK5cuUwMjJCoVAwZswY5syZg5ubG25ubsyZMwdjY2P8/f3fKMChQ4eyZMkSNm3aRIMGxXsxhLKgffv2CCHYv38/AQEB2g6nzMhIf8SNvxdgZduEap6Ti+2Y6bwIkU3EuZmkJt+hmudkLGy80dO30HZYUhlk5/wf7Jz/86+PF+U0fUqlUn1VU9sWLFiAtbU1Fy5cwMLCIl/PUSgUNG7cmMaNGxMTE8O6devYunUr/v7+GBgY4OrqSsWKFalQoQIVKlTA2dkZb29vEhIS+PTTT2nXrh0LFy5UD58wNjamf//+9O/f/1+Pqaury8CBA+nYsSMzZsxg+fLlJCcno1QqycjIwMTEhG+//ZaBAweWuNm7inOuJYegaE6B3sVf3ATy8iTta9asYeDAgQBMmDCBtLQ0RowYwaNHj2jQoAGhoaGYmZm9UYB6enrMnDnzjTuVpFl2dnbUrVuXkJCQUp2AF7eZUJITL5Gd9RQXjxElNvkGuHdzC08SLlDdZzEW5T21HY5Uxsm5sHN79OgRurq6XLhwgZo1a2JlZVWgBNbe3p7AwEACAwO5c+cOu3bt4tatW8TFxREXF0d4eDirV69Wj4Vu0KABS5YseeMk2cbGhm+//ZZvv/1WXZaUlMTYsWMZNGgQW7Zs4YMPPsDb25u///6bPXv20LlzZ44cOUKbNsVzClSZa5UNBR6CkheFQsH06dOZPn36GwV0586dXGV9+/alb9+SsxpZadehQweCgoLIzs7W2Nyy0uulp91HqTRA3/Dfb6Yp7rKz0oi6thpbp/9gbOZManIk6Wn3MbOsjq6eqbbDk0oxmWjn3/z582nVqhUtWrQAQF9fH3t7e+zt7alVqxYff/wx1atXz9e+nJ2dGT16dK7yrKwswsPD0dfX55133tFo/ADm5uasWrWK9u3bM3/+fPr06aN+zMvLi86dOxMQEMCxY8dwd3fX+PHfVnHOtUQhLZojF+KRpHzo0KEDs2bN4tChQ8X2CkJp8yztAQZGFUrcV6k5KJTo6BpxP2oX9yN3qIvtnLvhWiP3m7QkvQmZbL8dd3d3oqOjuXjxIjdv3iQmJoaYmBju3bvHrl27CA4Opl27dowfP56WLVu+0WuSrq4uNWvWLIToc+rRowc9evTg9u3b3Lp1C0dHR5ydnQkJCcHCwoKlS5cSHBxc6HFI0qvIBFwqsAYNGlC/fn0+//xzWrVqJRdKKgLpaQ8wMC4eY0TflI6OATUafs3DmN8wMXdDoVBy9exUdPXe7CtTqWyTiXbhUSqV1K5dm9q1a+coz8jIYMuWLSxevJjWrVvTokUL5s6dW+zvz3JxcVHfyPhiHu7atWsTFRWlzbBKJJUKVIUwXlv16omHSjWZOUkFplQqmTlzJmfPnuWvv/7SdjhlQnrqfQyMSnYCDmBi7oq9a2/Snt4lKuJHdPRMsansl/cTpTLtxO5muTap6Onr69OvXz/OnTvHrl27iI+Px8fHh9WrV2s7tALz8PAgLCyMxMREbYcilVHyCrj0Rnx9fVEqlZw9e1Y9DZJUOIQQpKc9wNqu5CcdKU+uc/nUp6hUGZSzqk3V2hPkUvTFwKP401w9M5VKrr0wNKlEavJt9A3LY23bGAOjgs8r/KZkYl0yKBQKOnfujLOzM7Vq1SrydTc0YdiwYSxcuJCJEycSHBz8ym9y09PTUSqVpXZq4Tchx4BrjkzApTdiYmJCvXr1mD59OvXr16dWrVraDqlUEqosblxcRFZmEuaW+bvxqTiLiw4hKzMJI1NHnibfRldX3nxZHGSkxaHKTiP6+toc5XdvbKB+m22FckyZbJd8P//8M1ZWVnTp0kXboRSYjY0NCxcuZOTIkYSHh1O+fHkePXpEYmIiiYmJPHr0iNTUVGxsbJg/fz4DBgwo2ffgSMWOTMClN7Zt2zY6depE48aN2bp1q7whU8NU2elcOz+LxAdhuNWZRLlSMG2fQvn8SlJayvOxl2lPozAyrfy6p0hFwMS8ao7fTcu5o9TRw8xCcx/69m/xlVcSS5nffvuNVq1aFWgF0OLko48+wtHRkYULF5Keno6ZmRmGhoZUr14dZ2dnnJ2dmTVrFh988AFOTk7qmWHKMjkPuObIBFx6Y/b29hw9epRevXrRoUMHjh8/jo+Pj7bD0hhtzgWelZXK1TOTSU68xLves7Cq2EgrcWiardN/UOoYkpp0i6TEv7Ao76XtkCTA1OIdvFv9QmbGI5Q6BhibOr3Rfl51VfvFCo5S6ZORkYGRkZG2w3grHTt2pGPHjly/fp3q1aurb9L8pzp16tCoUel4DX5bMgHXHJmAS2/FzMyM3bt3U79+fcaNG8ehQ4cwNDTUdlglllBlcT9yF9E31qPKTsejwQLKWdfO+4klhJFJJRzd+nP6t97YVGqNUsdA2yFJ/2VgZIOBUf7nmZdDSMo2IQSOjo788ccf2g5FIw4cOEBmZiaenp5Uq1YNV1dXqlSpgq2tLW3btkVXV6ZLkmbJHiW9NV1dXRYvXkybNm3w8PBgy5Yt1KtXT9thlTgZzxKIOD+bJwkXqFC5DQ7VBmJobKftsDTuUfxpMtMTqODQQduhSAUgE27pn4KDg9m+ffsbLwRT3NSvXx8fHx8ePHjA1q1bycrKwsjIiFatWnH16lW8vLx45513qFixYpmeeleFQFUIN0yqkFfAJemNNG/enIsXL9K7d28+/vhjTp06JW9Yyaf0tHju3dzMg6jd6OiaUKPhYspZ19F2WIUm4f5xjEydMC3npu1QpH8hk20Jnl/ljouLw9jYONcS57Vr10ZfX5/w8HBUKlWJT0q9vb05efIk8Hz2k8uXL3Po0CH27NnDlClTSEtLA54v2e7m5kadOnVo2rQpvXv3xsLCQouRSyWVTMAljXF3d2fu3Ll07NiR48eP07RpU22HVKylpz3g7o0NPIjei46OIZWqBmDv0q3UL0yTlPg3FuW9tR2G9F8y2ZZe5c6dO3h6evL48WOUSiU+Pj58+OGH+Pv7o6+vT6NGjdi0aRM9e/bExsaGb775ptRcdDEwMKBu3brUrVuX8ePHk5WVxbVr17h58yZRUVGEh4dz/vx5tmzZwpgxY+jRowdDhw6lSZMm2g690Mkx4JojE3BJo9q3b4+VlRV9+/YlJCQk10pq0nNxd/dz/cJcAHT1y+HVYiO6eiZajkpzVNkZKJR6ud6QM54l8OzpXczfGaSlyCSZcEv5ceHCBR4/fkzt2rXp0aMHJ06c4IMPPmDq1KkEBgYyfPhwunXrxvLlyxk2bBgKhYLFixeXyrHSurq6VK9enerVc84KFBMTw08//cSqVavYtGkTvXv35vvvv8fY2FhLkUolScn+zkgqdhQKBYmJicTExFCnTh1mzZql7ZCKFZUqEyGyuXMlWF1m79y9VCXfsbe3cXKvHzf++irXY0mJFwEwt5LzxhcFuYKk9Kb8/PwYP348ERERLFy4EF9fXw4fPkzz5s0ZNWoUbdq0ISYmhqFDh7J8+XKCgoJo3759mVpZ0t7enokTJxIREcH333/Pjh07aNiwIbdu3dJ2aIXmxUI8hbGVNTIBlzRu0qRJ6p+nTJmCQqHA2dmZffv28fDhQ9LT00v1H5sQApUqK0fZs9RYbvy9iJMhbfh9Tyuc3x2Os8dIAKKvryU9LV4boWqUEIJ7Nzdz6/K3ADxJuJCrzoPoPRibOWNgWL6IoysbZLItaYqxsTELFizg1q1bDBgwgNmzZ9OqVSvu3r1L9+7dOXHiBJ06dQJg+PDhHDhwgHPnzlG/fn3Onj2r5eiLlkKh4IMPPuDUqVM8ffoUb29vQkNDtR2WVMyVvu+KJK2bNWsWPXr0IDAwUP0iFBkZSfv27dV1dHV1MTMzw9TUFDMzM/z8/Jg3b16JuJEn41kCSY8ukfksgezsZ6iy08nKSiEtOZLUlCgyMx4B4OIxAptKrbn8xwRSHoern29iXhXLig3Q07fA0NiOiHMzuB+1C6d3BmvrlDQi8cEJ7oQHU6lKX/QNrLkdHoQQKhSK5/+nj+L+4HH8ady9Z2o50tJBJtjS62RkZBAZGYmpqSm2trZvPD7b1taWr7/+milTprBjxw7+7//+jx07dpCVlYW9vb26XosWLTh9+jTdu3enXr16DBkyhK+//rrEzxNeEDVr1uT06dMEBATQrl07PvvsM2bNmlWqFqASKoFKjgHXCJmAS4WiTp067N+/n4cPH/Lzzz8zcuTzq73r1q1DCEFycjIpKSkkJyfz8OFDgoKCCAsLY8uWLVqOPDchsom/d5AnD8+RlHiRZ6kxwPNVHXV0DFHqGKKja4yRiQMVKrdB38Ca1JQ73Lr0Nc+exqiTbwNjOzzqf4WxqaN63xY29dA3qkBWxhOtnJsmZfz3Kr6T+4fc+HshxqYu6uQbIPr6OsytamFVsbG2QizRZMIt5Ud0dDTjx49n7969JCcnA2BlZYWPjw/dunWjQ4cO2NkVfHrT8uXLM2TIEIYMGUJiYiIhISF4eeVcSMvV1ZXTp0+zfPlyRo8eTbNmzQgICNDIeZUUlpaW7N69mwULFjBlyhQAvvoq93C8kkrehKk5MgGXClX58uUZMWIEI0aMeG29QYMG0aNHD5o0acJ3333H77//jo+PDwYG2l+oJebWL9wJD35+5bqCD+ZWtTCzqvHaYRSP4v7gfuQu0p89pLrPIgyN7TEw+t9VKFV2Ovcjd3P35kYyMx5jYt6nqE6nUGRnpXHv1hYsbOqhUChJfnQ5xzhvIQSpybepXDWg1MyUUJhksi29qTFjxhAWFsaECRNo1KgRSUlJXLp0iYMHDzJ06FBUKhUuLi5UrVqVcuXKYWFhgZ+fH++9916+b6C0srLi/fffV//+6NEjQkJCSElJ4enTp4SHP7/o8M8r5GWJjo4OEydO5M6dO6xbt65UJeCS5sgEXCoW6tWrx9mzZ5kwYQKAerjKe++9x48//qiVr/Dq+20n5tYvxNz6BTuX7rhWH5Xv5yY9ugSAUqmHiXlV9PTLqR/LzEgi/PQXJD++QoVKflR2ex8jk8oaj78oRV//icz0R1T3WUx2VhppKZHYOv1H/XhW5hOys55iaFw235DzIhPuwnfjxg2mT5/O6dOncXR0xNXVFRcXlxz/WllZlegPiGfPnmXbtm38+OOPDBgwQF3etWtXJk+eTFxcHEePHuX333/n7t27PHnyhIiICH744QcqVKiAi4sL9vb22NnZYWdnl+vn8uXL5xomGBcXh6+vLzdu3ECpVGJiYoK5uTlTpkyhRYsWRd0ExUZaWhpHjhyhQYMG2g5FowrrhsnSfF/Yv5EJuFRslC9fnhUrVhASEsKxY8c4cOAAU6ZMoWrVqnz55Zdvvf/Hjx+zf/9+4uPjefr0Kampqep/y5cvT40aNYiOjubChQtcuHCBS5evPJ+fu0pvHKoNLNCxHNz6o0DJvVs/E38vFD0DSwwMK6BnYEVq8i2ys9Ko1WgZZpYeb31e2paZ8ZjYO9uxd+2FkUklhFBhbOZC5NXvUSh0sHP+D8+ePh+2Y2hSScvRFg8y4S5ct2/fZs6cOaSkpKBUKrly5QqXLl3C3t6eLl26EBsby5kzZ9iyZQtPnvxv+JeOjg46OjpYWlri6upKu3btaNOmDe7u7lhaWmrxjPJnxowZuLu757g6/U8VKlSgZ8+e9OzZM0f52bNn2b59OzExMcTExHDixAliY2OJj895c7iuri4VK1akcuXKuLu74+TkxNatW0lKSuL69etUqVKlxHyAiY2N5dSpU5w9e5Znz55Rrlw5KleuTPXq1bG0tCQtLY27d+8SHh6Onp4e3t7eeHp6Ym5uDjxPGF93rhMmTCAyMpKtW7cW1SlJJYxMwKViqXbt2nh7e5OZmcmXX35Ju3btaNiwYYH3k5qayo4dO9iyZQv79u0jIyMDAwMDjI2NMTExwdjYGGNjY2JiYtQrvtWqVYvGjRuTpmhLebvm6OoXfGEcpVIPx3c+wM75PRLjTpKeFkd62gMy0xOxsKmHvWuvHGPBS7LY29tRZT/D2rYJ6Wlx6OqZUqPhEm5fWc6ty19jWaE+8fd+Q1fPDKNScs4FJRPuonPjxg2aNm2KEAIPDw8yMzOpW7cuw4cP5/3338fEJOeUn48ePeL27dvcunWL+Ph4VCoVCQkJXLp0iUWLFjFt2jQAzM3NsbGxoWfPnnz55Zfo6+tr4/ReKykpiQoVKhQ4Cfby8so1nhue38j54MEDYmNjiYmJITY2ltjYWCIjIwkPD2f//v3UqFGDjRs3UrVqVU2dRqFRqVRs2bIFMzMz3N3dSUtLw9HRETMzM548eUJMTAwqlSrHc8qVK0dGRgZpaWkoFAoqVKhASkoKmZmZjBgxgunTp1Ou3P++4YyIiGDZsmUsW7aMb7/9Ntfc4SWdUKkQL7WRpvZb1sgEXCrWvvjiC/bu3Yu/vz/nzp3L91WohIQE1qxZw4IFC4iLi8PHx4evvvqKnj17UqlS7quwQggSEhKwtLRER0cHgMadj751/HoGFlR0aJ93xRLsUdwfAPx1YhgASqUBXi03UqXGGB49OMnty9/xOP5PKlXpi46O9sf0FwWZcGtP7969MTc35+jRo1SsWDHP+paWllhaWlK3bt1cj2VmZnLx4kWuX79OVFQUUVFRLF68mN9++40NGzbg5uZWGKfwxsaPH0/nzp3x8vLC39+fXr164eTk9Mb709fXx8HBAQcHBw1G+faePHlCaGgo27ZtY+/evSgUCipXrsySJUto3br1K5+TnJxM7969OXLkCJs2bWLFihW0bduWChUqqOs8e/aMiIgIUlJSMDIywtbWFltbW7Kzs7l69Spnz57lzp07mJubk5iYyJIlS/j55585c+YMdnZ2bN68mYCAAExMTFi4cKF68gFJehWZgEvFmq6uLhs3bsTLy4v333+f3bt3/+tUhUlJSezcuZPNmzerpz8cOHAggYGBeV6dUSgUlC8v56Z+E+/Wm83jh2fR0y+HENlcPTuN+JjfqOTaCzuXbkRH/IiufjnsXXtpO9RCIxPu4iEzM5Pz588zZ86cfCXfedHT01MvSf7CwIED6dWrFx4eHvTv3x9/f3+aNGlSLK6Id+zYkX379vH9998zdepUJkyYgK+vL5999hldunT51yvjDx48UC+znpSURGpqKvb29rz77rt4eHioL0oUlEqlIjMzU729mH72TQghmD9/PitXrlQvdOPp6cmYMWMwNjZm3759tG/fntmzZzNmzBjS09OJjo4mKiqKyMhIli1bph4SkpGRQZ8+fXLdW2RoaEitWrkXCdPV1aVGjRrUqFEjR/nQoUPx9PTkk08+YcGCBQwaNIg+ffqwatUqDA0N3+g8iztVIU1DWBj7LO5kAi4Vey4uLmzYsIGOHTsyZcoURo4cibGxMQ8ePOD69etcv36dsLAw9uzZw7Nnz/D19WXp0qX06NFDI2/C0uvpG1pTobKf+ndLm/ok3j9BJdde2Lv2IjsrlXLWdUvVap8y4S6e9PT06NWrFzNmzGDnzp04OTnh7OxM27ZtNXZDoJeXFxcvXiQ4OJglS5awevVqzM3Nadu2LZ07d6ZDhw5YW1tr5Fhvws/PDz8/P5KTk9m1axcrV66ka9eueHt7s2HDBqpVq6auu3HjRpYtW8bJkyfVZQqFAkNDQ9LS0oDnQzB8fX3x8vLC09OTOnXq4OzsrE7ms7KyuHnzJqGhoaxatYqbN2+qE+6Xh3MA+Pj4MH78eLp161agoTJhYWFMnDiRfv36MX36dHx9fXF1dVU/Pm7cOAIDA/n888+ZOnUq6enp6seUSiX169cnLCwMd3d3QkJC8t+gr+Hg4MCyZct4//33+eWXX7CxsWH58uWlNvmWNEsm4FKJ0L59e6ZPn860adOYM2dOjsdeXLWYOXMmvXr1wtGxbI4zLi4UCh2UyudXAx89+J246L1YVvDRclRvZ/8W31K1mEZpFhwcTI0aNbhx4waRkZGcOHGCr776iv379+Pn55f3DvLB2NiYcePGMXbsWC5cuMCvv/7K7t276d+/P0qlki+++IIvv/xSqwuLmZmZERAQQEBAAEePHmXo0KF4eXmxceNGOnXqxLhx41i6dClt27Zl/fr11KpVCycnJ8zMzFAoFOpx8MePH+fEiROsWLGCuLg4ACwsLKhYsSJpaWncv3+fjIwMFAoF3bt3p1+/fujr66Onp5drS0pKYtOmTfTo0YM+ffoQHBycY/z06yQkJACwaNEibGxscj2uq6vLokWLGDBgAKGhodjb2+Po6IiDgwP29vbqv9/MzEwNtfBzffr0wdHRkdDQULp06aK+SbO0krOgaI5MwKUSY8qUKXTt2pWoqCjS0tKwtrbGzc2NSpUqlYgVNMsCIVSkPLmGZYXnN8xGRfxIVmYyz57eg/K5x9gWVy+ucGdmZmrsaplUNCwsLJg8ebL6d5VKRZs2bRg2bBgXL17E1NRUY8dSKBR4enri6enJlClTiI2NZd68ecyaNQs7O7s81z8oKs2aNePMmTMMGDCAbt26MXToUIKCgli6dCmffPLJK59jbW1Ns2bNaNbsf9/2xMbGcv78ec6fP09iYiLGxsZUrFgRDw8P6tSpg5WVVZ6xDBo0iM2bNzNs2DCaN29OaGjoKxPql4WHh2NgYJDrJtqX1apV65XDSApTo0aNaNSoUZEeU1vkQjyaIxNwqcRQKBRaeXGV8u9x/BnS0x5gU7kNqcmRPHt6FwDLCsV7Llw5pKT0UiqVrFy5kpo1a+Lm5saoUaP4/PPPC2W6PDs7O77++mvS09MJDAykU6dOxeYbOTMzM37++WcGDhxIUFAQlStXLvBNgi/mBO/QocNbxdKnTx+qV69OmzZtqF+/PqtWraJly5b/Wv/WrVvMmTOH/v37Y2xs/FbHlqTiolReNszOztZ2CFIJp4kZUMqiB1G/YmzmipmFBw9jDgFgU6kNBkYV8nhm0Tuxu5l6k0q3KlWq8Mcff/Dee+8xadIkFixY8Eb7EUIwdOhQFAoFCoWCTZs2kZqamqveV199hbm5OWPHjn3b0DVKV1eXtWvXcujQIc6fP5/vlS8LQ82aNTl58iROTk506tSJq1evvrJeZmYmffv2xcbG5o3/3yTNeXEFvDC2sqZUXgHv1q0bu3fv1uhXjZIkvV7GswQSHpzAtfooVKoMoq+vBZ4vSlQcyES7bKtZsyZBQUGYm5szadIkXFxcaN68OWZmZujp6aFUKnNdFVepVKxevZqwsDC+/fZbfv31V77//nucnJyIjIzE398fe3t7Ro4cSfv27dXjnO/evYuzszM7duzg8ePHWFhYaOekX0FHR6fYrFDp4uJCSEgIzs7OrF27lrlz5+aqM3nyZM6dO0dYWFi+x4tLUklQKhPw06dP065dOw4cOICRkZG2w5GkMiHu7n6UCj1sKrVGodDBsdogTC3ewchUO3MIy4RbepWZM2dy8uRJevXKPS2mg4MDDRs2JDAwkLp16/LZZ5+xePFiALZt20Zqaip9+/Zlw4YNPHnyhBs3bvDNN98we/ZsJk2alGNfrq6uzJo1642n3SsrjI2NqV27Njdu3Mj12JEjR5g/fz7z58+nfv36WohOepkKFSqh+UVzVMiFeEqFXbt20blzZ/r378+WLVvkDXqSVARSHl/FzKo6unrPEw6HakV/5Vsm3VJe9PT0OHToENeuXePKlSukpaWpp827ceMGe/bsoX79+kyaNInly5czdepUOnfuzMGDB9HT02PkyJEoFAosLCzw9vbmp59+Yvny5fz1118olUpMTU2pVKkSFhYWJWZZdm2rWLEix48fZ8OGDfTu3RtdXV0yMzP5+OOP8fX15dNPP9V2iJKkcaUyAX8x32m3bt2YPHlyrmnrJEnSLCEEKU+uYVXRt0iPKxNu6U3o6Ojg4eGBh4dHrsfmzJnDjBkzmDFjBvB81g4nJye8vb3/dX8mJiZlZhaMwjBt2jT8/f15//33WbZsGfv37+fHH3/kypUrnD17Vl5EK0aEqnBmLCmEi+rFXqlMwAG6du3KggULGD9+PJmZmfTv358aNWrIKxJSnuQNmAWX/Ogy6WkPsLZtUujHkkm3VJj09PSYOXMmtWvXJiIi4q2Wcpfyx83NjdOnT3Py5Enat2+Pp6cn9+/fZ9iwYXh6emo7PEkqFKU2AYfnK2M9evSIb7/9loULFzJ48GBWrlwpP01LkoY9ijuFUscQM6uaGt+3TLglbejRo4e2QyhzGjZsyM6dO+nduze+vr4sXLhQ2yFJL5HzgGtOqc5EFQoFs2bNIi4ujuDgYNasWcOgQYNeOW2UJElvzsKmHqrsdO7d3JjrsZQn1zl/dDCP4k/ne39yikBJKpuaNWvG/fv3CQ0NzXPRHanovVgJszC2sqZUXwF/wcDAgGHDhmFmZsaAAQPYt28fn3/+OcOGDcPQ0FDb4UlSiVfOujamFu48jj+bY9pBIQT3bm4kNfkmCbFHsbSp98rny0RbkiRJKktK9RXwl/n7+3Pt2jU6dOjAp59+SpUqVVixYkWZ/OQlvZoc//1mkhIvkvI4HDvn93KUx987wMOYwwBUdOyU4zF5lVuSJKlkUalUhbaVNWUqAYfnc7OuXr2a8PBwWrRowfDhw+nZsycpKSnaDk2SSqz7Ub9iZOqItV1TdZkQgusXns9AVM1zCn8dHyaTbkmSJEmiDCbgL7i5ubF+/Xq2b9/O/v378fHx4ezZs9oOS5JKpKSEv7Cs4INC8b+XlKzMJPXPV89+qY2wJEmSJA2SS9FrTplNwF/o2rUrf/zxB0IIvL296dSpEydOnJDDUiSpALIyk9A3LJ/jCvfJvZ0ICAjg7NmzcvpPSZIkSfqHMp+AA3h4ePD333+zYcMGbt26RZMmTXB2dmbcuHH8/vvvZXJskiTl14ndzdBRZjFuuHuOcqVSyfr166lbt66WIpMkSZI0SQhVoW1ljUzA/0tHRwd/f38uXbrEoUOH6NSpE5s2bcLX1xcHBwdGjx7NsWPHyM7O1naokqR1L4/lrlSpEnfv3tVyVJIkSZJUMsgE/CVKpZIWLVrw3XffcffuXY4dO0bPnj3Ztm0bzZo1w8XFhblz5xIfH6/tUCUNkzOgvN7rbqCsWrUqN27c0EJUkiRJUlGRY8A1Rybgr6Gjo0OTJk1YunQpUVFR/P7777Rp04YZM2ZQuXJl+vbty8aNG0lISNB2qJJUKPI7a4lMwCVJksqAwkq+y2ACXiYW4tEEpVJJw4YNadiwIfPnz2fVqlVs3ryZzZs3o1Qqadq0KWPGjJE3b0ol2ptOD2hlZSU/iEqSJElSPskr4G/A2tqaCRMmcO7cOe7du8fKlSsxMjICoFu3bkRERGg5QknKP03MzX3jxg2qVq2qwagkSZKk4kYlVIW2lTUyAX9L9vb2DB48mJ07dwJw8+ZNatSowaRJk3j69KmWo5Pyq6yN/9b0gjhXr17F3d0974qSJEmSJMkEXNP+/PNPPv/8cxYtWoSHhwc//PADFy5cIC0tTduhSWVcYa1CqVKpiIiIkAm4JElSKSdvwtQcOQZcwwwNDfnyyy/p378/n3zyCR9++CEACoUCZ2dn3n33Xd59913q1atHkyZNsLe313LEUmm2f4svenp6hXqM6Oho0tLSZAIuSZIkSfkkE/BCUqVKFX799VcePXrE1atXCQ8PV29bt25l0aJF6npNmjShadOmNGvWDFdXVy1HLpV0J3Y3IzMzk5CQkCI53smTJwFkAi5JklTKCaFCFMLihGVxIR6ZgBcyS0tL9ewp/3T//n2OHz/O8ePHOXbsGGvXrkUIQaNGjfDz88PKygpXV1c6dOggl/EuAid2NyvR48A1Pawkv1JSUggMDMTPzw8nJyetxJAfQgj5dyRJkiQVGzIB1xJbW1t69uxJz549AXj8+DGhoaGsXr2a4OBgEhMTycjI4JNPPmHJkiUyeZBy0VbS/YIQgokTJxIfH8/hw4eLZR9NTU1l4MCB/PLLL8ybN4/AwEBthyRJklRiFdZ4bTkGXNIaCwsLevXqRa9evYDnyc2KFSsYMWIEKSkprFixAh0dHS1HWXQ2btyIrq4u/v7+xTKx0xZtJ93/NGvWLL777juWLVumtaFT586dY9u2bezcuZNLly7xyy+/0KNHD/Xj8+fP55dffgGgWrVqWolRkiSptBBCVSjDReQQFKnYUCgUDB8+HBMTEwYOHMjTp0/56aefCv2GOm3LzMwE4KOPPiItLY2DBw+yatUqDhw4gJ2dHbVq1dJyhEWvOCXdLxw5coRp06bx5ZdfMnLkyCI/flhYGKNGjeL8+fM5yqOionL8/uJD686dO+nSpUuRxSdJkiRJryMT8GKuX79+mJiY0KdPHxISEhg+fDjNmzfHyspK26FpnEqlokuXLowaNYrg4GDOnTvH119/zeXLlzl9+jTwfJrHevXqcerUKSwsLDR6419xGgdeHJPuF27cuEH//v1p1qwZkyZNKvLjb9q0CX9/f/Xv/fr1IyAggObNm2NgYJCjbmBgIJ988gnm5uZFHaYkSVKpo1KBqhCGixTCfZ3FnpwHvATo1q0bu3fv5s6dO3Tv3p3y5ctTt25dxo8fT0hICMnJydoOUSOePHnC77//DsDs2bNZs2YN8HxxIwBdXV0sLS25ffs2DRs25N1332Xy5Mk8efJEazFrUmHN060p+/bto06dOri5ufHs2TN++uknrQyLerHS7NChQ4mMjOSnn36ibdu2uZJvAH19fZl8S5IkScWOTMBLiLZt2xIREUFkZCRr1qyhZs2abN68mY4dO2Jtbc2ECRNK7MqbGRkZBAUF5RjKEB0dTVJSEgBjx44Fno+LHzRoEA0aNFDXmz17NhYWFgwaNIiwsDCEKFk3chT3pPuFS5cu0aNHD8qVK8cvv/zC7du3cXBw0Eos06ZNU98j4ejoqJUYJEmSyiKhUhXaVtbIISgljKOjIwMGDGDAgAEIIbhx4wabNm1i7ty5/PzzzwQFBdGhQwdth5lvR44cYcSIEURERODj48OQIUMAWL16NUII3NzcqFGjBqampkRGRhIfH48Qgvj4+Bz7WbNmDWvWrMHDw4NvvvmGVq1aaeN08qW4J9sve/z4Me+99x6urq6EhIRgYmKi7ZAkSZIkqUSTCXgJplAocHNzY+rUqQQEBDBixAg6duxIjx49+Prrr4v1KptxcXGMHz+edevW0bBhQ86dO0etWrXUC8h07949xw2nY8aMyfH82NhYNm/ezI8//sjff/+tLr9y5QqtW7emU6dOBAUFFfgqbWGNAy9pSfcLKpWKgIAAHj58yJkzZ2TyLUmSVIbJaQg1Rw5BKSWqVKnCvn372LRpE8ePH8fd3Z1ly5apZxUpLtLT0wkODuadd95hz549/PDDD5w4caLAs5vY2dkxduxY/vrrL8LDw5k8eXKOxy9cuECjRo24cuWKJsMvsJIwvOR15s+fz969e9m0aRNVqlTRdjgFcv/+fRQKBQqFAm9vb7p27crChQuJjo7O1/NjYmLYtm0bQIkd3iVJkiQVT/IKeCmiUCjo06cPbdu25fPPP2fUqFGMHj2aihUrYmdnh729fa7Nzs6OqlWrUq5cuUKL69atW+zdu5d9+/Zx6NAhUlNTGTRoEF999RXly5d/6/27u7szc+ZMZsyYwfHjx3n48CE+Pj60a9eOxo0bs2fPnlwrkRamkpxw/1N6ejqzZ89m9OjRtGvXTl0+fvx4zMzMmDhx4itvfPw3GRkZREdHo6Ojg5OTU6HO756dnc3Vq1fVv9etW5c7d+4wefJkPvvsMxo3bkyfPn3o0aMH1tbWJCUl8eTJE548eUJiYiLr1q1j/fr16OnpsWnTJlxdXWnWrBndu3enc+fOhfr3IkmSVFzJecA1R14BL4UsLS0JDg7m/PnzrFy5khEjRtCgQQN0dHQ4f/4833//PR999BGdO3fG29sbW1tbRo8ene8rg/mRkJDA3LlzqVatGlWqVGHMmDGkpKQwdepULl++zKpVqzSSfP+TQqGgadOmdOvWDXt7e44dO0aNGjVo1aoVGzduzPd+3iSBLik3UxaErq4uenp6XLp0iZ9//pmsrCwAlixZwvTp06latSr79+9/7T7Cw8MZPHgwtWvXxtTUlKpVq+Li4oK/vz/Z2dkajVelUrF69WoaNmyIubk5LVq0wMnJiUWLFrFy5UpCQ0OJi4vjp59+wtzcnDFjxmBra4uenh7W1ta4urri6elJq1atCA0NZf78+dy4cQOASZMmERsbS79+/bCxsaFr167cvXuXadOmsXLlSiIiIlCVwZuIJEkqW14MQSmMraCCgoJwcXHB0NAQLy8vjh8//tr6R48excvLC0NDQ1xdXQkODn7TZtAIeQW8FKtTpw516tR55WPZ2dnExcURGxvLnj17WLp0KcHBwXzwwQcEBga+8cqGV69eZenSpfz000+oVCr69u3L/PnzadmyZZFPB2dhYcH+/fv58MMPCQgIYO/evXz33Xcai6M0JduvoqOjQ1BQEFOnTqV3797Url2bBQsWsHXrVt577z3u3r1Lu3bt6N69O0uWLMkx3l4IwQ8//MDo0aOxtbXFz8+PoUOHUq1aNW7evMnIkSNRqVS0aNGCnTt3cufOHVQqFbVq1cLT01O92draApCVlUV4eDjx8fFkZGSQnp5ORkYGCoUCFxcXUlJSGD9+PGfOnKFLly7MmDGDhg0b4uPjg1L5v+sM5ubm9OvXj379+pGQkEBISAiZmZmUK1cux+bs7Iy+vr56CNfo0aP59NNPiY6OZvv27cydO5fatWuTmJio3reZmRmenp5MmjQJPz+/IvpfkiRJKnu2bNnCmDFjCAoKwtfXlxUrVtC+fXuuXLnyytmxbt++TYcOHfjwww9Zv349YWFhjBgxAhsbG7p3766FM5AJeJmlo6ODnZ0ddnZ21K1blzFjxhAcHMzChQtZtWoVAQEBBAYG4uHhkee+hBAcOHCApUuXsnfvXmxtbfniiy8YNmwYNjY2RXA2/87IyIj169fTrl07RowYQVhYGJs3b6Z+/fpvvM/Snnj/U58+fejTpw9nz55l+PDh+Pn5oauri4eHB6dOnWL37t18+umnuLu7M3XqVEaPHk1cXBwBAQGEhYUxdOhQli5dipGRkXqfbdq0wcbGhg8++ICtW7fSokUL2rdvj0ql4sKFC8yfP189t3vFihVxcnLi0qVLpKamvjZWLy8vTpw4ga+vb77Ozdramn79+hWoPRwcHBg9ejQdO3akXbt26gR88ODBuLm5sWfPHtq1a8fs2bOZOHFioQ6zkSRJKmqFNWVgQfe5ePFiBg8erJ45benSpezfv5/ly5czd+7cXPWDg4NxdHRk6dKlALz77rucOXOGhQsXygRck17MH12UMjMzSU1NJSkpqcQuFz9s2DD69+/P2rVr1VexnZycaNy4MU2aNKFx48aUK1eOw4cPc+fOHVJSUkhOTubw4cNcvXqVmjVrEhwcTLdu3dRjgwv6f1FY7dilSxdq1qzJ4MGDady4MV9//TUBAQH/Wj9kgyd+vU6ofw/9ubH6Z230r4LSdDu6ublx8OBBTp06xY4dOxg6dChCCDp16kTTpk2ZM2cOX3zxBdOmTcPAwABLS0u2bt1K69atyczMzHUzcJs2bbh48SJCiFyrugohiIyM5O+//+bvv/8mOjqazp074+Xlhb29Pfr6+hgYGKCnp0d2dja3b98mOTmZpk2bolQqNfr/82/taGNjw59//smDBw+wsLDA2NgYeL440Ny5c/niiy84efIky5cvx8zMTGPxlESl4bWxOJDtqBnabseS8P7xOtlZhXND+ov9vtw+BgYGue41ysjI4OzZs0ycODFHuZ+fn3oxv5edPHky1zeTbdu2ZdWqVWRmZmrnb0qUImlpacLW1lYAcpOb3OQmN7nJTW7FbrO1tRVpaWnaTpkKpCjyK1NT01xl06ZNyxXLvXv3BCDCwsJylM+ePVtUq1btlfG7ubmJ2bNn5ygLCwsTgIiJidFYOxVEqboCbmhoyO3bt8nIyNB2KJIkSZIkSbno6+tjaGio7TAKpCjyKyFErmF7r5tp6+W6r3p+XvVfVV5USlUCDs87SUnr2JIkSZIkScVZccmvypcvj46ODvfv389RHhcXR8WKFV/5HFtb21fW19XVxdrautBifR05DaEkSZIkSZJUIujr6+Pl5cWBAwdylB84cIBGjRq98jkNGzbMVT80NBRvb2+t3VMhE3BJkiRJkiSpxBg3bhw//PADq1evJjw8nLFjxxIVFcXw4cMB+Pzzz+nfv7+6/vDhw4mMjGTcuHGEh4ezevVqVq1axfjx47V1CqVvCIokSZIkSZJUevXu3ZuEhARmzJhBbGwsNWrUICQkBCcnJwBiY2OJiopS13dxcSEkJISxY8fy3XffYW9vzzfffKO1KQgBFOLFKHRJkiRJkiRJkgqdHIIiSZIkSZIkSUVIJuCSJEmSJEmSVITKZAJ+7NgxOnfujL29PQqFgh07duR4XAjB9OnTsbe3x8jIiObNm3P58uUcddLT0xk1ahTly5fHxMSELl26cPfu3Rx1Tp48SZ06dXBycuL7779Xl/v4+PDRRx/lqLt8+XIUCgWrVq3KUT548OB/vau3OEpOTmbMmDE4OTlhZGREo0aNOH36tPrxgQMHolAocmw+Pj459nHt2jV8fX2pXLkyM2bMUJf36dOH9u3b56i7d+9eFAoFU6ZMyVE+c+ZM7O3tC+EMC19ebZif/lnW2zArK4vJkyfj4uKCkZERrq6uzJgxA9U/ljuWfTFv+WlH2R/z5uzsnKuvKRQKRo4cCci+mF95taPsi1KJopXlf7QsJCRETJo0SWzdulUAYvv27TkenzdvnjAzMxNbt24VFy9eFL179xZ2dnYiKSlJXWf48OGiUqVK4sCBA+LcuXOiRYsWonbt2iIrK0tdx93dXfzf//2fCAsLE1WqVBGRkZFCCCEmTpwo3nnnnRzH7NWrl3BwcBABAQE5yl1dXcUXX3yh4RYoPL169RIeHh7i6NGj4vr162LatGnC3Nxc3L17VwghxIABA0S7du1EbGysektISMixj1atWonly5eLM2fOCG9vb3HixAkhhBDBwcHC1NRUZGZmqutOmDBBODg4CF9f3xz7aNmypfD39y/ksy0cebVhfvpnWW/DWbNmCWtra/Hrr7+K27dvi19++UWYmpqKpUuXquvIvpi3/LSj7I95i4uLy9HPDhw4IABx+PBhIYTsi/mVVzvKviiVJGUyAf+nlxNwlUolbG1txbx589Rlz549E+XKlRPBwcFCCCEeP34s9PT0xObNm9V17t27J5RKpdi3b5+6zNHRUdy6dUukpKQIb29vcfnyZSGEEPv378+1/GnFihVFUFCQqFSpkrosKipKAOLAgQMaP+/CkJqaKnR0dMSvv/6ao7x27dpi0qRJQojnbzT/+c9/XrsfLy8vcerUKZGRkSG6dOki9uzZI4QQ4tq1awIQJ0+eVNetX7+++O6774S+vr54+vSpEEKI9PR0YWRkJL7//nsNnl3RyKsN89M/hSjbbSiEEB07dhSDBg3KUdatWzfx/vvvq3+XfTFvebWj7I9v5pNPPhFVqlQRKpVKCCH74pv6ZzvKviiVNDIBfykBv3nzpgDEuXPnctTr0qWL6N+/vxBCiN9++00AIjExMUedWrVqialTp6p//+GHH4SBgYHQ0dERH3/8sbo8JSVF6OnpiY0bNwohhLh8+bIwNzcXz549E2ZmZiIiIkIIIcTatWtz/OEXd0lJSQIQBw8ezFHu4+MjmjVrJoR4/kZTrlw5YWNjI9zc3MSQIUPEgwcPctTfs2ePMDMzE7q6uqJr1645vlWwt7cXc+bMUR9PV1dXxMXFiXfffVeEhoYKIYQ4evSoAMSNGzcK8WwLR15tmJ/+KUTZbkMhhJg7d65wcnIS165dE0IIceHCBVGhQgX135wQsi/mR17tKPtjwaWnpwtra2sxe/ZsdZnsiwX3cjvKviiVNGVyDPjrvFiq9OXlTCtWrKh+7P79++jr62NpafmvdeD5+O2EhATi4+P59ttv1eUmJibUq1ePI0eOAHDkyBEaN26MgYEBvr6+OcobNGiAsbGxpk+zUJiZmdGwYUNmzpxJTEwM2dnZrF+/nj/++IPY2FgA2rdvz4YNGzh06BCLFi3i9OnTtGzZkvT0dPV+OnToQHx8PDExMWzfvh0dHR31Y82bN1e3z/Hjx6lWrRo2NjY0a9YsR7s5ODhQpUqVIjt3TcmrDfPTP6FstyFAYGAgffv2xd3dHT09PTw9PRkzZgx9+/ZV15F9MW95taPsjwW3Y8cOHj9+zMCBA9Vlsi8W3MvtKPuiVNLIBPxfKBSKHL8LIXKVvexVdUxMTHIl6gAtWrTI8QfdvHlzgFx/6C1btnyzE9CSdevWIYSgUqVKGBgY8M033+Dv769+kevduzcdO3akRo0adO7cmb179xIREcGePXty7MfAwAAbG5tc+2/RogVhYWFkZmaWqnb7p7zaEPLXP8tyG27ZsoX169ezceNGzp07x9q1a1m4cCFr165V15F9MW/5aUeQ/bEgVq1aRfv27XPcxCf7YsG9qh1B9kWp5JAJ+EtsbW0BcnxiBoiLi1N/sra1tSUjI4NHjx79a528tGjRgoiICO7du8fRo0dp1qwZ8L8/9KioKG7fvk2LFi3e9pSKVJUqVTh69CgpKSlER0fz559/kpmZiYuLyyvr29nZ4eTkxPXr1/O1/xYtWvD06VNOnz7N4cOHc7Tb6dOnSUxM5OTJkyWu3f7pdW2Yn/6Zl7LQhp999hkTJ06kT58+1KxZk379+jF27Fjmzp37r8+RfTG3vNpR9seCiYyM5ODBgwwZMuS19WRffL1XtaPsi1JJIxPwl7xIcg4cOKAuy8jI4OjRo+rpAL28vNDT08tRJzY2lkuXLuV7ysBGjRphYGBAUFAQaWlpeHl5AeDt7c2TJ09YsWIFhoaGuaaiKilMTEyws7Pj0aNH7N+/n//85z+vrJeQkEB0dDR2dnb52m+VKlVwcHBg165dXLhwQf0CaWdnh7OzM4sWLeLZs2el4gXyVW2Yn/6Zl7LQhqmpqSiVOV/edHR0ckyf9zLZF3PLqx1lfyyYNWvWUKFCBTp27PjaerIvvt6r2lH2RanE0eL4c61JTk4W58+fF+fPnxeAWLx4sTh//rx6msB58+aJcuXKiW3btomLFy+Kvn37vnIawsqVK4uDBw+Kc+fOiZYtW+aahjAvTZs2FWZmZqJdu3Y5ylu3bi3MzMxEy5YtNXPCRWjfvn1i79694tatWyI0NFTUrl1b1K9fX2RkZIjk5GTx6aefit9//13cvn1bHD58WDRs2FBUqlQpR9vmpX///sLMzEy4u7vnKB8yZIgwMzMTrq6umj6tIvW6NhQif/0zL6W9DQcMGCAqVaqknj5v27Ztonz58mLChAlCCCH7Yj7l1Y5CyP6YX9nZ2cLR0VEEBgbmKJd9sWD+rR2FkH1RKlnKZAJ++PBhAeTaBgwYIIR4PrXWtGnThK2trTAwMBBNmzYVFy9ezLGPtLQ08fHHHwsrKythZGQkOnXqJKKiogoUx7Rp0wSQY9okIYSYOXOmAMTMmTPf6jy1YcuWLcLV1VXo6+sLW1tbMXLkSPH48WMhxPMp9vz8/ISNjY3Q09MTjo6OYsCAAQVutzVr1ghADB8+PEf5unXrBCAGDx6ssfPRhte1oRD56595Ke1tmJSUJD755BPh6OgoDA0Nhaurq5g0aZJIT08XQsi+mF95taMQsj/m14vpZ1/MKPOC7IsF82/tKITsi1LJohBCiKK84i5JkiRJkiRJZZkcAy5JkiRJkiRJRUgm4JIkSZIkSZJUhGQCLkmSJEmSJElFSCbgkiRJkiRJklSEZAIuSZIkSZIkSUVIJuCSJEmSJEmSVIRkAi5JkiRJkiRJRUgm4JIkSZIkSZJUhGQCLkmSJEmSJElFSCbgkiRJkiRJklSEZAIuSZIkSZIkSUXo/wEfnud0+gI1ewAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature\n",
    "\n",
    "# Create a plot\n",
    "fig, ax = plt.subplots(figsize=(12, 6), subplot_kw={'projection': ccrs.PlateCarree()})\n",
    "\n",
    "#ax.set_extent([-93, -87, 28, 32], crs=ccrs.PlateCarree())\n",
    "\n",
    "# Plot the data\n",
    "data.plot(ax=ax, transform=ccrs.PlateCarree(), cmap='coolwarm', cbar_kwargs={'label': 'Cyano'},)\n",
    "\n",
    "# Add coastlines\n",
    "ax.coastlines()\n",
    "\n",
    "# Add state boundaries\n",
    "states_provinces = cfeature.NaturalEarthFeature(\n",
    "    category='cultural',\n",
    "    name='admin_1_states_provinces_lines',\n",
    "    scale='50m',\n",
    "    facecolor='none')\n",
    "\n",
    "ax.add_feature(states_provinces, edgecolor='gray')\n",
    "\n",
    "# Optionally, add gridlines, labels, etc.\n",
    "ax.gridlines(draw_labels=True)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://i.imgur.com/BCKIguq.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What you can do next about Cyanobacteria using PACE?\n",
    "\n",
    "Spectral Angle Mapper: Spectral similarity \n",
    "Input: library of Cyanobacteria bloom Rrs spectra with Chla at different levels\n",
    "\n",
    "Spectral Mixture Analysis: unmix different cyanobacteria species based on spectral difference.\n",
    "\n",
    "![](https://i.imgur.com/xLaLMA4.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}